The Epidemiology of Diabetes Mellitus: An International Perspective

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The Epidemiology of Diabetes Mellitus

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The Epidemiology of Diabetes Mellitus An International Perspective Edited by

Jean-Marie EkoeÂ

Centre de Recherche CHUM, Montreal, Canada

Paul Zimmet

International Diabetes Institute, Caulfield, Victoria, Australia and

Rhys Williams

Nuffield Institute for Health, Leeds, UK Foreword by

Sir George Alberti

JOHN WILEY & SONS, LTD

Chichester


New York
Weinheim
Brisbane
Singapore
Toronto

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Copyright # 2001 by John Wiley & Sons Ltd, Baffins Lane, Chichester, West Sussex PO19 1UD, England National 01243 779777 International (‡44) 1243 779777 e-mail (for orders and customer service enquiries): [email protected] Visit our Home Page on http://www.wiley.co.uk or http://www.wiley.com All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London, W1P 0LP, UK, without the permission in writing of the publisher. Other Wiley Editorial Offices John Wiley & Sons, Inc., 605 Third Avenue, New York, NY 10158-0012, USA WILEY-VCH Verlag GmbH, Pappelallee 3, D-69469 Weinheim, Germany John Wiley & Sons Australia, Ltd., 33 Park Road, Milton, Queensland 4064, Australia John Wiley & Sons (Asia) Pte Ltd, 2 Clementi Loop #02-01, Jin Xing Distripark, Singapore 129809 John Wiley & Sons (Canada) Ltd, 22 Worcester Road, Rexdale, Ontario M9W 1L1, Canada

Library of Congress Cataloging-in-Publication Data The epidemiology of diabetes mellitus : an international perspective = edited by Jean-Marie EkoeÂ, Paul Zimmet, Rhys Williams. p. cm. Includes bibliographical references and index. ISBN 0-471-97448-X (cased) 1. Diabetes± Epidemiology. I. EkoeÂ, J.M. II. Zimmet, Paul. III. Williams, D.R.R. (David Robert Rhys) RA645.D5 E654 2001 614.5 0 9462Ðdc21

00± 069341

British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN 0-471-97448-X Typeset in 10=12 Times from the author's disks by Mathematical Composition Setters Ltd, Salisbury, Wiltshire Printed and bound in Great Britain by Antony Rowe Ltd, Chippenham, Wiltshire This book is printed on acid-free paper responsibly manufactured from sustainable forestry, in which at least two trees are planted for each one used for paper production.

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Contents About the Editors . . . . . . . . . . . . . . . . . . . . . . . .

vii

Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ix

Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii Sir George Alberti Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . J.-M. EkoeÂ, R. Williams and P. Zimmet

xv 1

PART I: DEFINITIONS AND EVIDENCE FOR PREVENTION 2

The Clinical Syndrome and the Biochemical Definition . . . . . . . . . . . . . . . J.-M. Ekoe and P. Zimmet

7

3

Diabetes Mellitus: Diagnosis and Classification. . . . . . . . . . . . . . . . . . . . . . . J.-M. Ekoe and P. Zimmet

11

4A Prevention of Type 1 Diabetes Mellitus. J.S. Skyler, A. Pugliese, C. Bernal and J.B. Marks

31

4B Epidemiology, Evidence for Prevention: Type 2 Diabetes . . . . . . . . . . . . . . . . . . . . 41 P. Zimmet, M. de Courten, A.M. Hodge and J. Tuomilehto 5

Methodology for Physical Activity Assessment. . . . . . . . . . . . . . . . . . . . . . . . . E.W. Gregg and A.M. Kriska

51

7A Type 1 Diabetes: Global Epidemiology. M. Karvonen, A. Sekikawa, R. LaPorte, J. Tuomilehto and E. Tuomilehto-Wolf

7B Type 1 Diabetes: Prediction Based on the Genetic-Epidemiological Facts in the 90s . . . . . . . . . . . . . . . . . . . . . . . . . 103 A. Green and K.O. Kyvik 7C Type 1 Diabetes: Atypical Diabetes in Young People Across the World . . . . . . 113 R.B. Lipton 8A Type 2 Diabetes: Aetiology and Environmental Factors . . . . . . . . . . . . . . 133 J. Mann and M. Toeller 8B Type 2 Diabetes: Genetic Factors . . . . . 141 G. Velho and P. Froguel PART III: NON-CAUCASIAN POPULATIONS 9A Non-Caucasian North American Populations: African Americans . . . . . . 157 M.A. Banerji and H. Lebovitz 9B Non-Caucasian North American Populations: Native Americans . . . . . . . 181 K.M. Venkat Narayan, R.G. Nelson, R.L. Hanson, D.J. Pettitt and W.C. Knowler 10

Mexico . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 I. Lerman-Garber, F.J. GoÂmez-PeÂrez and R. Quibrera-Infante

11

Latin America . . . . . . . . . . . . . . . . . . . . . 205 L.J. Franco and S.R.G. Ferreira

PART II: CAUCASIAN POPULATIONS 6

Ascertainment, Prevalence, Incidence and Temporal Trends . . . . . . . . . . . . . . . . R. Williams

65

71

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vi

CONTENTS

12

The Middle East . . . . . . . . . . . . . . . . . . . 217 H. King, G. Roglic and A. Alwan

13

Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 A.A. Motala, M.A.K. Omar and F.J. Pirie South East Asia . . . . . . . . . . . . . . . . . . . . 233 A. Ramachandran, V. Mohan, B.A.K. Khalid and A. Vichayanrat

14

15

Pacific Island Populations. . . . . . . . . . . . 239 D.J. McCarty and P. Zimmet

16

China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 P.H. Bennett, Gungwei Li and Pan Xiaoren

17

Japan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 N. Tajima, M Matsushima, S. Baba and Y. Goto

PART IV: ASSOCIATED RISK FACTORS AND COMPLICATIONS 18

19

20

Malnutrition-related Diabetes Mellitus: Myth or Reality? . . . . . . . . . . . . . . . . . . . 263 J.-M. Ekoe and J. Shipp Type 2 Diabetes and Obesity . . . . . . . . . 273 A.M. Hodge, V.R. Collins, P. Zimmet and G.K. Dowse Epidemiology of the Insulin Resistance Syndrome . . . . . . . . . . . . . . . . . . . . . . . . . 285 B. Balkau and E. EschweÁge

21A Long-term Complications: Diabetes and Coronary Heart Disease . . . . . . . . . . . . . 301 E. Barrett-Connor and K. PyoÈraÈlaÈ

21B Long-term Complications: Diabetes, Stroke and Lower Extremity Arterial Disease . . . . . . . . . . . . . . . . . . . . 319 E. Barrett-Connor and K. PyoÈraÈlaÈ 21C Long-term Complications: Diabetic Neuropathy . . . . . . . . . . . . . . . . 327 A.J.M. Boulton 21D Long-term Complications: Diabetic Nephropathy . . . . . . . . . . . . . . . 337 K. Borch-Johnsen 21E Long-term Complications: Diabetic Retinopathy . . . . . . . . . . . . . . . . 349 C.A. McCarty, C.A. Harper and H.R. Taylor 22

Diabetes Mortality . . . . . . . . . . . . . . . . . . 369 T.A. Welborn

PART V: IMPLICATIONS 23

Economic Costs. . . . . . . . . . . . . . . . . . . . . 383 T.J. Songer

24

Diabetes Field Surveys: Theory and Practical Aspects . . . . . . . . . . . . . . . . 399 G.K. Dowse

25

The United Kingdom Prospective Diabetes Study: An Epidemiological Perspective . . . . . . . 425 P. Zimmet, M. Cohen and J.-M. EkoeÂ

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431

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About the Editors Professor Jean-Marie Ekoe is Professor of Medicine, Endocrinology, Metabolism and Nutrition, Faculty of Medicine, University of Montreal, Quebec, Canada. He is a member of the Epidemiology Research Unit, Research Centre of the Centre Hospitalier Universitaire de MontreÂal (CHUM). He was the first recipient of the World Health Organization and International Diabetes Federation Kelly West Memorial Lilly Award in 1983. His major clinical and research interests are in the epidemiology of diabetes, diabetic foot problems and other long-term complications of diabetes mellitus. Professor Paul Zimmet is Foundation Director of the International Diabetes Institute, Professor of Diabetes, Monash University, Melbourne and Head of the WHO Collaborating Centre for the Epidemiology of Diabetes Mellitus and Health Promotion for Non-Communicable Disease Control. His major research interest relates to the health effects of lifestyle change in newly industrialized nations in the Pacific and Indian Ocean region and the socio-economic and public health aspects of diabetes in these populations. He was

the recipient of the 1991 ADA's Kelly West Award, the 1994 Eli Lilly Award of the IDF and in 1997 received the inaugural Peter Bennet Award of the International Diabetes Epidemiology Group, for outstanding contributions to research in the field of epidemiology of diabetes. Professor Rhys Williams is Professor of Epidemiology and Public Health at the Nuffield Institute for Health, University of Leeds, United Kingdom. His major research interests include epidemiology and health care research in diabetes and in other longterm health problems such as multiple sclerosis. He is currently a member of the Expert Reference Group advising the UK Department of Health on the Diabetes National Service Framework and the Chair of the International Diabetes Federation's Task Force on Diabetes Health Economics. In the past he has been Consultant Advisor on Public Health to the Chief Medical Officer of England. He was recently awarded the Wilfrid Harding Prize for Services to the Faculty of Public Health Medicine as well as the Professor Viswanathan Diabetes Research Centre Gold Medal Oration Award, 1997.

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Contributors Ala'din Alwan Regional Adviser Non-communicable Diseases, WHO Regional Office for the Eastern Mediterranean, Alexandria, Egypt S. Baba

WHO Collaborating Center, Kobe, Japan

Beverley Balkau INSERM U258, Epidemiologie Cardiovasculaire et Metabolique, HoÃpital Paul Brousse, 16 Avenue Paul Vaillant-Couturier, F-94807 Villejuif cedex, France Mary Ann Banerji Department of Medicine, State University of New York, Health Science Center at Brooklyn, Box 123, 450 Clarkson Avenue, Brooklyn NY 11203-2098, USA Elizabeth Barrett-Connor Professor and Chief, Division of Epidemiology, Department of Family and Preventive Medicine, UCSD School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093-0607, USA Peter H. Bennett Chief, Biometrics and Data Management Section, PECRB, NIDDK, 1550 East Indian School Road, Phoenix, Arizona 85014, USA Knut Borch-Johnsen Steno Diabeter Center, Niels Steensens Vej 2, 2820 Gentofte, Denmark Andrew J.M. Boulton Professor of Medicine, University of Manchester, Consultant Physician, Manchester Royal Infirmary, Oxford Road, Manchester M13 9WL, UK M. Cohen International Diabetes Institute, 260 Kooyong Road, Caulfield, Melbourne, Victoria 3162, Australia Veronica R. Collins International Diabetes Institute, 260 Kooyong Road, Caulfield, Melbourne, Victoria 3162, Australia M. de Courten International Diabetes Institute, WHO Collaborating Centre for Epidemiology of

Diabetes Mellitus, 260 Kooyong Road, Caulfield, Victoria 3162, Australia Gary K. Dowse Medical Epidemiologist, Communicable Disease Control Branch, Public Health Division, Health Department of Western Australia, PO Box 8172, Perth Business Centre, Perth, Western Australia 6849, Australia Jean-Marie Ekoe Professeur en MeÂdecine, Endocrinologie Metabolism et Nutrition, Centre de Recherche CHUM, Campus Hotel Dieu, 3840 rue St-Urbain, Montreal, Quebec H2W 1T8, Canada Eveline EschweÁge INSERM U21, Faculty of Medicine Paris-Sud, Villejuif, France Sandra Roberta Gouvea Ferreira Department of Social Medicine, Faculdade de Medicina de RibeiraÄo Preto, Universidade de SaÄo Paulo, Avenida Bandeirantes 3900, 14049-900 RibeiraÄo Preto ± SP, Brasil LaeÂrcio Joel Franco Professor, Department of Social Medicine, Faculdade de Medicina de RibeiraÄo Preto, Universidade de SaÄo Paulo, Avenida Bandeirantes 3900, 14049-900 RibeiraÄo Preto ± SP, Brasil Philippe Froguel CNRS EP10, Institut Pasteur de Lille et CHU, Lille, France Francisco J. GoÂmez-PeÂrez Departamento de Diabetes y Metabolismo de Lipidos, Instituto Nacional de la NutricioÂn Salvador Zubiran, Vasco de Quiroga #15, Tlalpan 14000, Mexico City, Mexico Y. Goto Tohoku Kosei-Nenkin Hospital, Miyaginoku, Sendai 983, Japan Anders Green Department of Epidemiology and Social Medicine, University of Aarhus, Vennelyst Boulevard 6, DK-8000 Arhus C, Denmark

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x

CONTRIBUTORS

Edward W. Gregg Department of Epidemiology, Graduate School of Public Health University of Pittsburgh, 130 DeSoto Street, Pittsburgh PA 15 231, USA

Israel Lerman-Garber Departamento de Diabetes y Metabolismo de Lipidos, Instituto Nacional de la Nutricion Salvador Zubiran, Vasco de Quiroga 15, Tlalpan DelegacioÂn, CP 14000, MeÂxico City, MeÂxico

Robert L. Hanson Diabetes and Arthritus Epidemiology Section, National Institute of Diabetes and Digestive Kidney Disorders, Phoenix, Arizona, USA

Harold Lebovitz SUNY Health Science Center, Brooklyn Department of Medicine, Box 50, Clarkson Avenue, Brooklyn, NY 11203, USA

C. Alex Harper University of Melbourne Department of Ophthalmology, Royal Victorian Eye and Ear Hospital, 32 Gisbourne Street, East Melbourne VIC 3002, Australia

Gungwei Li China± Japan Friendship Hospital, He Ping Li, Beijing, China

Allison M. Hodge Epidemiologist, International Diabetes Institute, 260 Kooyong Road, Caulfield, Melbourne, Victoria 3162, Australia Marjatta Karvonen Diabetes and Genetic Epidemiology Unit, Department of Epidemiology and Health Promotion, National Public Health Institute, Mannerheimintie 166, 00300 Helsinki, Finland B.A.K. Khalid Dean, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, 50300 Kuala Lumpar, Malaysia Hilary King Medical Officer, Division of Noncommunicable Diseases, World Health Organisation, 1211 Geneva 27, Switzerland William C. Knowler Diabetes and Arthritus Epidemiology Section, National Institute of Diabetes and Digestive Kidney Diseases, Phoenix, Arizona, USA Andrea M. Kriska Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, 130 DeSoto Street, Pittsburgh PA 15261, USA Kirsten O. Kyvik The Danish Twin Registry, Epidemiology Institute of Public Health, University of Southern Denmark Ð Odense University, Denmark R. LaPorte Diabetes and Genetic Epidemiology Unit, Department of Epidemiology and Health Promotion, National Public Health Institute, Helsinki, Finland

Rebecca B. Lipton Division of Epidemiology and Biostatistics, University of Illinois at Chicago, School of Public Health (M=C 922), 2121 W Taylor Street, Chicago IL 60612, USA Jim Mann Department of Human Nutrition, University of Otago, PO Box 56, Dunedin, New Zealand M. Matsushima WHO Collaborating Center, Kobe International Conference Center, 8th Floor, Minatojima-nakamachi, Chuo-ku Kobe 650, Japan Daniel J. McCarty Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, 32 Gisborne Street, East Melbourne, Victoria 3002, Australia Catherine A. McCarty A=Professor, University of Melbourne Epidemiology Research Unit, Royal Victorian Eye and Ear Hospital, 32 Gisbourne Street, East Melbourne, Victoria 3002, Australia V. Mohan Managing Director, Madras Diabetes Research Centre, 4 Main Road, Royapuram, Chennai-600 013, India A.A. Motala Deputy Head, Diabetes Endocrine Unit, Faculty of Medicine, Department of Medicine, University of Natal, Private Bag 7, Congella, Durban 4013, South Africa Robert G. Nelson Diabetes and Arthritus Epidemiology Section, National Institute of Diabetes and Digestive Kidney Diseases, Phoenix, Arizona, USA Mahomed A.K. Omar Diabetes Endocrine Unit, Faculty of Medicine Department of Medicine, University of Natal, Private Bag 7, Congella, Durban 4013, South Africa

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CONTRIBUTORS

Pan Xiaoren China ±Japan Friendship Hospital, He Ping Li, Beijing, China David J. Pettitt Diabetes and Arthritus Epidemiology Section, National Institute of Diabetes and Digestive Kidney Diseases, Phoenix, Arizona, USA Fraser J. Pirie Diabetes Endocrine Unit, Faculty of Medicine, Department of Medicine, University of Natal, Private Bag 7, Congella, Durban 4013, South Africa Kalevi PyoÈraÈlaÈ Department of Medicine, University of Kuopio, PO Box 1627, FIN-70211, Kuopio, Finland Ricardo Quibrera-Infante Centro MeÂdico del PotosõÂ, Av PososõÂ 425 Col Lombos, San Luis PotosõÂ 78210, Mexico A. Ramachandran Deputy Director, Diabetes Research Centre, 4 Main Road, Royapuram, Chennai-600 013, India Gojka Roglic Medical Officer, Division of Noncommunicable Diseases, World Health Organisation, 1211 Geneva 27, Switzerland A. Sekikawa Diabetes and Genetic Epidemiology Unit, Department of Epidemiology and Health Promotion, National Public Health Institute, Mannerheimintie 166, 00300 Helsinki, Finland J. Shipp Endocrinologie Metabolism et Nutrition, Centre de Recherche CHUM, Campus Hotel Dieu, 3840 rue St-Urbain, Montreal, Quebec H2W 1T8, Canada

xi

Hugh R. Taylor University of Melbourne Department of Ophthalmology, Royal Victoria Eye and Ear Hospital, 32 Gisborne Street, East Melbourne, VIC 3002, Australia Monika Toeller Diabetes-Forschungsinstitut au der Universitat Dusseldorf, Auf'm Hennekamp 65, D-40225 Dusseldorf, Germany J. Tuomilehto National Public Health Institute, Mannerheimintie 160, Helsinki FIN 00300, Finland Eva Tuomilehto-Wolf Professor, Diabetes and Genetic Epidemiology Unit, Department of Epidemiology and Health Promotion, National Public Health Institute, Mannerheimintie 166, FIN-00300 Helsinki, Finland Gilberto Velho INSERM U-342 HoÃpital SaintVincent-de-Paul, 82 Avenue Denfert Rochereau, 75014 Paris, France K.M. Venkat Narayan MRCP, DDT, CDC, K-68, 4770 Buford Highway NE, Atlanta, GA 30341, USA A. Vichayanrat Chief, Division of Endocrinology and Metabolism, Department of Medicine, Siriraj Hospital Medical School, Bangkok 10700, Thailand Timothy A. Welborn Clinical Professor, Departments of Medicine & Public Health, UWA, Physician in Endocrinology & Diabetes, Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Hospital Avenue, Nedlands, Western Australia 6009, Australia

Thomas J. Songer Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh PA 15261, USA

Rhys Williams Professor of Epidemiology and Public Health, Division of Public Health, Nuffield Institute for Health, 71± 75 Clarendon Road, Leeds LS2 9PL, UK

Naoko Tajima Department of Medicine, Jikei University School of Medicine 3 Nishihinbashi Minato-Ku, Tokyo 105, Japan

Paul Zimmet Professor & Director, International Diabetes Institute, 260 Kooyong Road, Caulfield, Victoria 3162, Australia

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FOREWORD In recent decades, there has been a relative absence of new books in the area of epidemiology of diabetes. Yet, since the publication of Dr Kelly West's landmark book on the Epidemiology of Diabetes and its Vascular Complications in 1978, great changes have taken place. The World Health Organization has revised the criteria and classification of diabetes on several occasions, as have the American Diabetes Association. Meanwhile the long-awaited results of the United Kingdom Prospective Diabetes Study (UKPDS) were published and the Diabetes Prevention Program has reported as this book goes to press, alas too late for inclusion in this book. A profusion of studies worldwide have confirmed that an epidemic of Type 2 diabetes, as part of the `globalization' process, is occurring across the world affecting particularly developing countries. Some of the most important developments in diabetes have emerged through epidemiology. The rising prevalence and incidence of Type 2 diabetes in many populations has stimulated research on the genetic, environmental, behavioural, socioeconomic and cultural factors contributing to the epidemic. This highlights the sociological aspects of epidemiology and public health. The main intention of this book is to mainly help disseminate the most recent epidemiological data about diabetes mellitus among the many different people involved in diabetes health care, including diabetologists, internists, nurses and other health care providers and health decision makers. A glance at the book's list of contents shows that diabetes has no boundaries, no frontiers and is indeed an international problem. In the discipline of epidemiology, it's important to consider study methodology and design in interpreting epidemiological reports. Many factors should be considered such as the definition of diabetes mellitus, populations differences and various other adjustments. The editors and authors have paid careful attention to these problems while reviewing the international literature. The importance of standardization of methodology must be emphasized. It is not always possible to make direct

comparison in terms of diabetes risk, prevalence, incidence and complications rates between different populations. However, an international approach focuses primarily on geographically specific aspects of the disease using the same definition. Epidemiology is not a static science. The recent advances that have been accomplished so far in different epidemiological studies have increased our knowledge about different types of diabetes worldwide. Is it still worthwhile to continue to perform large scale population surveys except in communities where that information is lacking? Some background information is needed before attempting interventions. Although some regions and populations still have to be evaluated, it is time for action. Primary prevention of diabetes mellitus should become a reality. So, a second and very important aim of this book is to provide the current evidence for primary prevention. The time is ripe to take action in places where the prevalence of diabetes is known and is increasing. This book will hopefully help those who have a specific interest in public health to use adequate tools in measuring the impact of diabetes in a given population. Studies from China, Finland and the USA have clearly shown that weight control and adequate physical activity substantially reduce the risk of developing diabetes. However, it is equally clear that many people do not respond to lifestyle advice. Therein lies a major challenge. The burden that diabetes places on individuals and societies is huge and difficult to evaluate. The medical impact includes mainly the socio-economic costs that treatment of complications and early death impose. The book gives a clear view of this important component of diabetes. In spite of dramatic developments in the treatment of diabetes and its complications, primary prevention of diabetes remains somehow untouched in most countries. The hope is that attitudes will change once this book has been read. Sir George Alberti

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Acknowledgements The making of a book is always a joint venture that could be quite painful. Although the pain and despair were there sometimes, we are profoundly grateful to several friends, colleagues and mostly contributors who have fought to ensure the survival of this book. Our thanks go first to our contributors from the five continents. They never let us down. The team at John Wiley & Sons has been very supportive and tolerant. It's been a joy to work with them throughout the last five years. Deborah Reece, Michael Osuch, Hannah Bradley, Judy Marshall and Dr Lewis Derrick have been outstanding. Let's not forget our local `keepers of

the flame': our secretaries in Montreal (Sylvie SauveÂ), in Melbourne (Lesley Anderson and Sue Fournel), and in Leeds (Pam Lillie ). We are particularly grateful to them. Finally we must thank our families for providing us time, support and always understanding. Jean-Marie Ekoe Rhys Williams Paul Zimmet Montreal, Leeds, Melbourne August 2001

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1

Introduction Jean-Marie EkoeÂ, Rhys Williams, Paul Zimmet

Twenty three years ago Dr Kelly West published the first volume on the epidemiology of diabetes and its vascular complications (1). He left his own unique memorial in a book that critically reviewed more than 2000 papers. This outstanding review gathered most of the contributions, clinical and population-based, on the subject of diabetes epidemiology and highlighted the many gaps in our diabetes epidemiology knowledge at that time. Much has happened in the last two decades. The present volume bridges the more than twenty years that have elapsed since Dr Kelly West's milestone monograph and we hope it will provide a stimulating `state of the art' review of recent epidemiological studies spanning the globe. The book presents and discusses the new diagnostic criteria and classification of diabetes. At the end of the 1970s confusion reigned both with regard to the classification of diabetes and to the appropriate diagnostic tests and their interpretation. Enormous variation in diagnostic cutoff values, in size of the glucose load and clear definition of types of diabetes prevailed. In 1979 and 1980 the National Diabetes Data Group (NDDG) in the USA (2) and the World Health Organization (WHO) Second Expert Committee on Diabetes (3) brought some order. Further revisions have resulted in new recent classification and diagnostic criteria that seem to be more consistent and less controversial (4, 5). One of the major changes in the provisional World Health Organization Consultation report is the disappearance of the Malnutrition Related Diabetes Mellitus (MRDM) as a major category (4). Whilst the protein-deficient pancreatic diabetes (PDPD) variant of MRDM has been dropped, the former fibrocalculous pancreatic diabetes (FCPD) variant is now part of the other types category which include all those types where aetiology is more

clear. A chapter discusses this issue. One major difference remains in Gestational Diabetes Mellitus (GDM). The American Diabetes Association (ADA) has not changed its testing and criteria whilst WHO includes both impaired glucose tolerance (IGT) and new diabetes in pregnancy under the banner of GDM (4, 5). Compared to what was reigning in the 70s this is `order out of chaos'. However, there is still room for improvement. The available diagnostic criteria and classification have been widely used since the early 80s in numerous epidemiological studies, allowing comparisons between countries, regions and different populations worldwide. The results of these studies, suggest that the prevalence of diabetes will dramatically increase in the next quarter of this century both in the developed and the developing countries. The World Health Organization (6) suggests an increase worldwide of the prevalence of diabetes in adults of 35% and an increase in the number of people with diabetes of 122%. The developing countries will face an increase of 48% in the prevalence of, and an increase of 170% in the number of people with, diabetes compared to an increase in the prevalence of diabetes of 27% in developed countries, with an increase of 42% in the number of people with diabetes. Although caution should be expressed regarding these figures due to the lack of suitable survey data, and extrapolations in some places and countries, the epidemic nature of diabetes in the world is supported by studies summarized in this book. The likely burden of diabetes during the first years of the twenty-first century should not be overlooked: Figures of 135 million adults with diabetes in 1995 rising to probably 300 million in

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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2

THE EPIDEMIOLOGY OF DIABETES MELLITUS

year 2025 are not far from reality and may even underestimate the magnitude of this major public health problem. Application of new diagnostic criteria will probably add another 2% in the prevalence of diabetes. The greater longevity of women likely explains the fact that there are more women than men with diabetes in many countries. The increasing concentration of diabetes in urban areas of developing countries is notorious and clearly emerges from the reported surveys. There is now considerable evidence that Type 2 diabetes is lifestyle-related. Given the dramatic change of lifestyle in many developing nations, researchers have had great opportunities to study the genetic and environmental determinants of Type 2 diabetes through both cross-sectional and longitudinal studies. The book presents an extensive overview of these studies and focuses on evidence for prevention of diabetes. In the last 20 years dramatic changes in the management of Type 1 diabetes have positively modified the natural history of this disorder. The benefits of tight metabolic control have been demonstrated in numerous studies and most conclusively in The Diabetes Control and Complications Trial (DCCT) for Type 1 diabetes (7) and in the United Kingdom Prevention Diabetes Survey (UKPS) for Type 2 diabetes (8±11). An upsurge of interest in diabetes epidemiology that started in the early 80s was immensely reinforced. Although incidence and prevalence data have added only limited information to our further understanding of the aetiology of diabetes, their importance in adding to our knowledge of the public health implications of this disease is considerable. Evidence for prevention is surely emerging and is thoroughly discussed in this volume. Following the euphoria of the discovery of insulin in the 20s appeared the recognition of most of the disorders due to diabetic complications. The natural progression of the disease to nephropathy and retinopathy led to renal failure and blindness. The consequences of cardiovascular disease and neuropathy resulted in early cardiovascular death, foot disease and amputations. Although it is now possible to reduce the incidence of complications, or, when they occur, retard their progression, their prevalence and incidence remain unacceptably high. The book addresses the magnitude of diabetic complications, time trends and geographical variations.

Proper care of diabetes in the 2000s implies identification of all patients with diabetes and early detection of complications which will enable care providers to take the steps needed to combat the disease. This may not be possible at all in the absence of epidemiological data. This book will therefore be a very useful tool for diabetes care providers, researchers and public health experts. It provides a global picture of the characteristics of the epidemic nature of diabetes and its complications. It is hoped that those of all disciplines involved in diabetes, regardless of their fields of expertise, will find both interest and practical help from its content. REFERENCES 1. West KM. Epidemiology of Diabetes and its Vascular Lesions. New York, Elsevier, 1978. 2. National Diabetes Data Group. Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes (1979); 28: 1039± 1057. 3. WHO Expert Committee on Diabetes Mellitus. Second Report. Technical Report Series 646. Geneva, WHO, 1980. 4. Alberti KGMM, Zimmet P, for the WHO Consultation. Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications. Part 1: Diagnosis and Classification of Diabetes Mellitus Ð Provisional Report of a WHO Consultation. Diabetic Med (1998); 15: 539± 553. 5. The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care (2000); 23 (suppl). 1: 54 ± 519. 6. King H, Aubert RE, Herman WH. Global burden of diabetes, 1995± 2005: prevalence, numerical estimates and projections. Diabetes Care (1998); 21(9): 1414±1431. 7. The Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med (1993); 329: 977± 986. 8. UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with Type 2 diabetes: UKPDS 33. Lancet (1998); 352: 837± 853. 9. UK Prospective Diabetes Study (UKPDS) Group. Effect of intensive blood-glucose control with metformin on complications in overweight patients with Type 2 diabetes: UKPDS 34. Lancet (1998); 352: 854± 865.

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INTRODUCTION

10. UK Prospective Diabetes Study (UKPDS) Group. Tight blood pressure control and risk of macrovascular and microvascular complications in Type 2 diabetes: UKPDS 38. Br Med J (1998); 317, 703±713.

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11. UK Prospective Diabetes Study (UKPDS) Group: Efficacy of atenolol and captopril in reducing risk of macrovascular and microvascular complications in Type 2 diabetes: UKPDS 39. Br Med J (1998); 317, 713± 720.

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Part I

Definitions and Evidence for Prevention

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2

The Clinical Syndrome and the Biochemical Definition 1

2

Jean-Marie EkoeÂ,1 Paul Zimmet2

Centre de Recherche CHUM, Montreal, Canada International Diabetes Institute, Melbourne, Australia

DEFINITION OF THE DIABETIC STATE Diabetes mellitus is a disease that was recognized in antiquity. Polyuric states resembling diabetes mellitus were described as early as 1550 BC in the ancient Egyptian papyrus discovered by George Ebers (1). The term `diabetes', which is from the Ionian Greek meaning `to pass through', was first used by Aretaeus of Cappadocia in the second century AD as a generic description of conditions causing increased urine output (2). The association of polyuria with a sweet-tasting substance in the urine was noted in the fifth to sixth century AD by two Indian physicians, Susruta and Charuka (1, 2). The urine of certain polyuric patients was described as tasting like honey, sticky to the touch and attracting ants. Two forms of diabetes could be distinguished in the Indians' descriptions: one affected older, fatter people and the other thin people who did not survive long; this strongly reminds us the present clinical description of Type 2 and Type 1 diabetes. The term diabetes mellitus, an allusion to the honeyed taste of the urine, was first used in the late eighteenth century by John Rollo and others (3) to distinguish it from other polyuric states in which the urine was tasteless. The concept that diabetes was a systemic disease arising in the blood was elaborated a century before (in the seventeenth century) by Matthew Dobson, a physician in Liverpool (England) who published a series of experiments showing that the serum of a patient with diabetes, as well as the urine, contained a sweet-tasty substance namely sugar (4). The nineteenth century is the key century that has greatly contributed to the understanding of

diabetes. Claude Bernard made numerous discoveries in the field of metabolism and diabetes. He described the storage of glucose in the liver as a glycogen and the acute hyperglycemia that followed experimental damage of the medulla oblongata known as `piquÃre' diabetes (5). Oskar Minkowski and Josef Von Mering noted that total pancreatectomy produced diabetes in dogs (6). The pancreatic islets were named after Paul Langerhans by Edouard Lafresse. Langerhans had suggested that pancreatic islets produced a glucose-lowering substance. This substance was named insulin by Jean de Meyer in 1909, almost a decade before insulin was discovered (7). Although diabetes mellitus has been recognized for many centuries and major advances have been accomplished since the discovery of insulin in the understanding of diabetes and metabolism, there was no clear or widely accepted definition of the diabetic state until the early 80s. In 1980, the World Health Organization (WHO) Expert Committee on diabetes mellitus (8) defined the diabetic state as a state of chronic hyperglycemia which may result from many environmental and genetic factors often acting jointly. Hyperglycemia is due to defects in insulin secretion, insulin action or both. This imbalance leads to disturbances of carbohydrate, fat and protein metabolism. The major effects of diabetes mellitus include long-term damage, dysfunction and failure of various organs. Diabetes mellitus may present with characteristic symptoms: thirst, polyuria, polydypsia, blurring of vision, weight loss, and infections. In its most severe forms, ketoacidosis or a non-ketotic hyperosmolar state may develop and lead to stupor, coma and, in absence of effective treatment, death. Most of the time, symptoms are

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

not severe, or may be absent, and consequently hyperglycemia of sufficient degree to cause pathological and functional changes may be present for a long time before the diagnosis is made. The longterm complications of diabetes mellitus include progressive development of disease of the capillaries of the kidney and retina, damage to the peripheral nerves and excessive atherosclerosis. The clinical manifestations of these complications therefore include nephropathy that may lead to renal failure, retinopathy with potential blindness, neuropathy with risk of foot ulcers, amputation, Charcot joints, and features of autonomic dysfunction, including sexual dysfunction. People with diabetes are at increased risk of cardiovascular, peripheral vascular and cerebrovascular disease. Diabetes mellitus is thus defined as a set of abnormalities characterized by a state of sustained hypgerglycemia. It is a clinical description with a chemical definition. Pathogenic mechanisms and various explanations, to be found, lie behind the sustained hyperglycemia. Processes which destroy the beta-cells of the pancreas with consequent insulin deficiency, and others that result in resistance to insulin action are part of a possible group of processes involved in the development of diabetes. THE CLINICAL SYNDROME The usual clinical symptoms of diabetes mellitus, polyuria and polydipsia are the direct result of the high blood glucose concentration. Weight loss in spite of polyphagia, ketoacidosis, visual changes, skin infections, sepsis and pruritus belong to the same list of symptoms. With mild hyperglycemia, these cardinal symptoms are lacking. The description of a clear clinical syndrome bears a very low incidence (1=10 000 per year) and prevalence (9). It is not surprising that few studies have systematically determined the frequency of various symptoms and their relationship to factors such as age, sex and degree of glycemia. The frequency of most symptoms is quite different in previously undiscovered diabetes, as contrasted with people with diabetes who have been under treatment for months or years. Other factors such as intensity of treatment, degree of acceptance of recommended therapy and age of onset, do affect the frequency of the different symptoms (9). The description of a

clear clinical syndrome which encompasses different and probably non-specific symptoms seems to be a poor definition criterion (10). Responses recorded from approximately 1700 diabetics in Bauer's study (10) pertaining to symptoms presented in the beginning of or during the disease, showed no specificity for diabetes. Almost 52% of the patients had none of these `diabetic symptoms'. No data were gathered in this study on the frequency of these symptoms in the general population or in people without diabetes. Welborn et al.'s study (11) was a controlled study. The authors compared rates of symptoms in known subjects with diabetes and in those found to be either affected or non-affected with diabetes in a survey. An increase of thirst was reported by 12% of the new `screenees' with diabetes, 13% of known people with diabetes and 5% of people without diabetes. Polyuria was reported by 28% of new cases, 13% of known people with diabetes and 11% of people without diabetes. Visual deterioration was reported by 35% of newly diagnosed cases, 31% of known individuals with diabetes and 25% of people without the disease. A history of pruritus vulvae was reported by 29% of both groups of women with diabetes and 15% of nonaffected women. In the Bedford survey (12), symptoms of diabetes were only slightly more common in those with 2 h blood glucose levels over 6.7 mmol=l (120 mg=dl) than in those with lower values. In several other studies, classical diabetes symptoms are lacking in more than 25% of newly diagnosed diabetics (9). The clinical recognition of glycosuria as the sole marker of diabetes is also unreliable. Quite a few patients with high renal thresholds or mild hyperglycemia may be missed. Furthermore, the number of false-positives is not minimized by this procedure in certain conditions. Therefore, the prevalence of diabetes will be underestimated when one restricts oneself to the classical syndrome. The use of blood glucose estimation (population screening) greatly raises the prevalence of diabetes when it is used instead of glycosuria determination. West, Keen and others have estimated that between the classical historical phase of ascertainment and the clinical=glycosuric phase, diabetes rates increased at least 10-fold. They increased 10-fold again with the epidemiological blood glucose screening phase (9, 13).

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CLINICAL SYNDROME AND BIOCHEMICAL DEFINITION

THE BIOCHEMICAL DEFINITION Hyperglycemia: the Common Factor The epidemiological attempt to study the natural history and pathogenesis of diabetes as a whole can only rely on one common and stable factor, high blood glucose, despite the wide variation in clinical manifestations and various contributing factors. However, high blood glucose alone does not answer all the questions. Over the past 30 years, evidence has accumulated that numerous and etiologically different mechanisms (genetic, environmental or immunologic processes) may play an important role in the pathogenesis, the clinical course and the emergence of complications of the `diabetic state' (9, 14). Does correction of hyperglycemia prevent all of the various pathologic changes observed with diabetes? There is some evidence that people with diabetes who are not treated develop more complications than wellcontrolled patients (9, 15, 16). However, there are few instances in which characteristic complications of diabetes have been described before hyperglycemia was observed. This indicates the vast heterogeneity of diabetes and illustrates the fact that it is not yet clear to what extent the long-term classical diabetic complications are the result of hyperglycemia, the generally accepted and fundamental factor for diabetes, or related factors such as, for instance, insulin deficiency, plasma or tissues osmolality changes, glycated proteins and lipid abnormalities. Despite these questions, hyperglycemia remains the most important factor required for the diagnosis of diabetes. SIGNIFICANCE OF BLOOD GLUCOSE IN POPULATION In different populations, the distribution of blood glucose may vary greatly. In one of the early studies of diabetes epidemiology performed in the USA and based on medical history, urine glucose tolerance in a defined community, Wilkerson and Krall reported a large variation in the distribution of blood glucose values within the eight screenee age groups (17). One pertinent fact was observed: higher levels of blood glucose were apparent with aging. In the 75 ±79 years age group, 50% of subjects had high blood glucose. This has been

9

confirmed in other health surveys (18). Where does diabetes start? In most populations, the degrees of hyperglycemia used for diagnosis of diabetes are based upon the findings in large, normal population samples and validated by prospective observations on outcome (13). However, there are still, to some extent, arbitrary lines across a continuous distribution of values in most populations (e.g., distribution of capillary blood glucose). Most unselected Caucasian populations display a unimodal distribution of glycemia:. 80 ±85% of people have normal blood glucose, 2 ±4% are in the diabetes range. Between these two extremes remains a third category of people neither frankly diabetic nor non-diabetic. These people have an unpredictable future. It has been recognized by both the National Diabetes Data Group (19) and the World Health Organization (8, 20) that this category of `borderline diabetics' includes a wide variety of subclasses that forms the new `impaired glucose tolerance' class which will be discussed in more detail in the next chapter. Epidemiological observations of Pacific Islanders (9, 21, 22), Arizona Pima Indians (23) and Tamilspeaking South African East Indians (24) and other non-Caucasian populations have brought to light new patterns of blood glucose distribution. They have shown that blood glucose values were bimodally distributed. In the Pima and Nauru population studies, a 100 g glucose load was used. The 2 h plasma glucose values were bimodally distributed. When the 2 h plasma glucose was related to the rate of microvascular disease, it became evident that from about 11 mmol=l (180±200 mg=dl) of blood glucose, microvascular disease (retinopathy) increased significantly. In conclusion, the study of the distribution of blood glucose in a population seems to be prerequisite to any study of diabetes in that population. It has been a common observation that in populations with a high prevalence rate of diabetes, blood glucose values are bimodally distributed in those populations with a cut-off point around (11.1 mmol=l (200 mg=dl). It has also been assumed that in a general population, low levels of circulating insulin were apparent when blood glucose was around 11.1 mmol=l (200 mg=dl) (9, 13, 21 ±23). Mild, long-standing hyperglycemia might be a marker of a silent ongoing process resulting in damage of key organs.

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

REFERENCES 1. McFarlane Ian A, Bliss M, Jackson JGL, Williams G. The history of diabetes mellitus. In: J Pickup, G Williams (eds), Textbook of Diabetes 2nd edn, vol. 1. London, Oxford, Blackwell Science, 1997: pp. 1.1± 1.21. 2. Papaspyros NS. The History of Diabetes Mellitus, 2nd edn. Stuttgart, Thieme, 1964. 3. Rollo J. An account of two cases of diabetes mellitus with remarks as they arose during the progress of the cure. London, C. Dilly (1797). 4. Dobson M. Experiments and observations on the urine in diabetes. Med Obs Inq (1776); 5: 298± 316. 5. Bernard C. LecËons de physiologie. Paris, BaillieÁre (1855): pp. 289; 296±313. 6. Von Mering J, Minkowski O. Diabetes mellitus nach Pankreasertirpation. Arch Expert Path Pharm Leipzig (1890); 26: 371± 387. 7. De Meyer J. Sur la signification physiologique de la seÂcreÂtion interne du pancreÂas. Zbl Physiol (1940); 18: S826. 8. Report of a WHO Study Group on Diabetes Mellitus. Diabetes mellitus. Technical Report Series 727. Geneva, World Health Organization, 1985. 9. West KM Epidemiology of Diabetes and its Vascular Lesions. New York, Elsevier, 1978. 10. Bauer ML. Characteristics of persons with diabetes. US National Center for Health Statistics. Vital and Health Statistics, Series 10, N  40: 1967. 11. Welborn TA, Curnow DH, Wearne JT, Cullen KJ, McCall MG, Stenhouse NS. Diabetes detected by blood sugar measurement after glucose load: report from the Busselton survey. Med J Austr (1966); 2: 778±83. 12. Keen H. The Bedford survey: a critique of methods and findings. Proc R Soc Med (1964); 57: 196± 202. 13. Keen H. Criteria and classification of diabetes mellitus. In: JI Mann, K PyoÈraÈlaÈ, A Teuscher

14. 15.

16. 17. 18.

19. 20. 21.

22. 23. 24.

(eds), Diabetes in Epidemiological Perspective. Edinburgh, London, Melbourne, New York, Churchill Livingstone, (1983): pp. 167± 182, 1983. Fajans SS, Cloutier MC, Crowther RL. Clinical and etiologic heterogeneity of idiopathic diabetes. Diabetes (1978); 28: 1112± 1125. Pirart J. Diabetes mellitus and its degenerative complications: a prospective study of 4400 patients observed between 1947 and 1973. Diabetes Care (1978); 1: 168± 188. Skyler JS. Complications of diabetes mellitus relationship to metabolic dysfunction. Diabetes Care (1979); 2: 499± 509. Wilkerson HLC, Krall LP Diabetes in a New England town. A study of 3156 persons in Oxford, Mass. J Am Med Assoc (1947); 135: 209± 216. National Diabetes Data Group. Diabetes in America. Diabetes Data Compiled 1984, US Department of Health and Human Services, NIH Publications N  85 ± 1468, 1985. National Diabetes Data Group. Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes 28: (1979); 1039± 57. WHO Expert Committee, Diabetes Mellitus, Second Report, Technical Report Series 646. Geneva, World Health Organization, 1980. Zimmet P, Taylor RR Whitehouse S. Prevalence rates of impaired glucose tolerance and diabetes mellitus in various Pacific populations according to the new WHO criteria. WHO Bull (1982); 60 (2): 279±82. Zimmet P, Taft P. The high prevalence of diabetes mellitus in Nauru, a central Pacific island. Adv Metab Disord 9: (1978); 225± 240. Bennett PH, Burch TA, Miller M. Diabetes in American (Pima) Indians. Lancet (1971); ii: 125±128. Jackson WPU. Epidemiology of diabetes in South Africa. Adv Metab Disord (1978); 9: 11 ± 46.

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3

Diabetes Mellitus: Diagnosis and Classification 1

2

Jean-Marie EkoeÂ,1 Paul Zimmet2

Centre de Recherche CHUM, Montreal, Canada International Diabetes Institute, Melbourne, Australia

DIAGNOSIS AND DIAGNOSTIC CRITERIA Diabetes mellitus may present with clear and classical symptoms (thirst, polyuria or ketoacidosis) or may be accompanied by specific complications. The lack of sensitivity and specificity of some of these `diabetic symptoms' has already been discussed (see previous chapter). However, when the symptoms and=or specific complications are present, the diagnosis of diabetes is confirmed by a single, unequivocally elevated blood glucose measurement as shown in Figure 3.1 (1). Severe hyperglycemia found under conditions of acute infective, traumatic or other stress may be transitory and should not in itself be regarded as diagnostic of diabetes. If a diagnosis of diabetes is made, one must feel confident that the diagnosis is fully established since the consequences for the individual are considerable and lifelong (2). For the asymptomatic persons at least one additional plasma=blood glucose measurement with a value in the diabetic range is essential, either fasting, from a random (casual) sample, or from the oral glucose tolerance (OGTT). Levels of blood glucose below which a diagnosis of diabetes is virtually excluded have also been defined (Figure 3.1). If different samples fail to confirm the diagnosis of diabetes mellitus, the person should be reassessed and retested until the diagnostic situation becomes clear. Additional factors such as family history, age, ethnicity, adiposity and concomitant disorders should be considered before deciding on a diagnostic or therapeutic course of action (2). A single abnormal blood glucose value should never be used as the sole basis of diagnosis of diabetes in an asymptomatic subject. An alternative to the single blood glucose estimation or OGTT has long

been sought to simplify the diagnosis of diabetes. Glycated hemoglobin reflecting average glycemia over a period of weeks, was thought to provide such a test. In certain cases, it gives equal or almost equal sensitivity and specificity to glucose measurement. However, lack of standardization and its unavailability in many parts of the world make it difficult to recommend it as a good alternative at this time (2, 3). In a collaborative study involving nine British towns over 2 years, of 254 newly diagnosed cases of diabetes aged 18 ±50 years, 81% were diagnosed on one single random=casual blood glucose measurement of 11.1 mmol=l (200 mg=dl) or more (4). Furthermore, a diagnosis of diabetes was established from casual blood glucose estimation without any glucose tolerance test in 800 patients (90%) attending the Diabetic Clinic at King's College Hospital in London (5). When symptoms are lacking and blood glucose levels are less markedly elevated (e.g., glucose concentration in a casual or random blood sample between 4.4 and 10.0 mmol=l for venous whole blood), measurements made after fasting or after a glucose load may be necessary to confirm or refute the diagnosis of diabetes. An entire investigation is needed if symptoms are questionable. In case of a medical, obstetrical or family history of diabetes, a single elevated blood glucose measurement may or may not be decisive. An oral glucose tolerance test (OGTT) is indicated in this situation (Table 3.1). Table 3.2 shows the diagnostic values of OGTT for diabetes mellitus and other categories of glucose tolerance abnormalities. Collecting and interpreting epidemiologic data implies a complete understanding of diagnostic methods and the criteria applied. It is therefore appropriate to review briefly the indications of one

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

Figure 3.1 Unstandardized (casual, random) blood glucose values in the diagnosis of diabetes in mmol=L (mg=dL). Reproduced from the 1985 WHO Study Group Report (1) by permission

of the most widely used and misused methods of diagnosis: the OGTT. THE DEMONSTRATION OF AN ABNORMAL BLOOD GLUCOSE LEVEL USING AN ORAL GLUCOSE TOLERANCE TEST (OGTT) No marker other than a high blood glucose level has been discovered to identify the diabetic state. The diagnosis depends heavily on the demonstration of abnormal blood glucose levels. Symptoms and signs of diabetes and urine glucose tests are non-specific tests for diabetes although they should be taken into account when present. Before performing the OGTT, it has been assumed that a random (or casual) blood glucose level should be obtained (Figure 3.1). Plasma venous glucose levels greater than 11.1 mmol=l (200 mg=dl) are usually diagnostic irrespective of time of day or status of fasting, provided the elevated blood glucose values are confirmed. The Oral Glucose Tolerance Test (OGTT) Conn and Fajans believed that the diagnosis of diabetes in a completely asymptomatic patient could be made only on the basis of a carefully performed glucose tolerance test (6, 7). Ten years earlier, Soskin believed that the OGTT was `practically worthless as it was used and inter-

preted' (8). These conflicting statements illustrate the fact that, as a clinical diagnostic tool, the OGTT has been grossly overemphasized and misused. It is now apparent that the OGTT is useful in clearly defined situations as summarized in Table 3.1. For instance, the OGTT has been of prime importance in many epidemiological surveys of diabetes and is still one of the best instruments in such studies. However, from a clinical viewpoint, the OGTT may be performed in specific circumstances. An equivocal random or casual blood glucose level (the diabetes mellitus uncertain zone as defined by the 1985 WHO Study Group on Diabetes Mellitus, Figure 3.1) deserves an OGTT. The establishment of a diagnosis is highly necessary in this situation. The OGTT might also be included as part of a special clinical investigation, or be needed for medico-legal reasons. If an OGTT is performed, it is sufficient to measure the blood glucose values while fasting and at 2 h after a 75 g oral glucose load (Annexes 1 and 2) (2). For children the oral glucose load is related to body weight, i.e. 1.75 g=kg. Diabetes in children usually presents with severe symptoms, very high blood glucose levels, marked glycosuria, and ketonuria. In most children the diagnosis is confirmed without delay by blood glucose measurements and treatment (insulin injections) is initiated immediately. A small proportion of children and adolescents, however, present with less severe symptoms and may require a fasting blood glucose and=or an OGTT for diagnosis. Diagnostic interpretations of the fasting and 2 h post-load concentrations in non-pregnant subjects are shown in Table 3.2. The diagnostic criteria in children are the same as for adults but in Table 3.1 Indications of oral glucose tolerance test (OGTT) 1. When a `random' blood glucose is equivocal e.g.: Fasting blood glucose: * > 6.1±7.0 mmol=l < (IFG) Post-prandial blood glucose: * > 7.8± 11.0 mmol= l 2. As part of special clinical investigation e.g.: Fasting glycosuria in pregnancy Data collection in certain endocrine or other diseases 3. For experimental and epidemiological purposes 4. To exclude diabetes mellitus or impaired glucose tolerance IFG: Impaired Fasting Glycemia *: Venous plasma values

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DIABETES MELLITUS: DIAGNOSIS AND CLASSIFICATION Table 3.2 Values for diagnosis of diabetes mellitus and other categories of hyperglycemia Glucose concentration mmol=l (mg=dl) Whole blood Venous Diabetes mellitus (DM): Fasting or 2 h post-glucose load or both Impaired glucose tolerance (IGT): Fasting concentration (if measured) and 2 h post-glucose load Impaired fasting glycemia (IFG): Fasting 2 h (if measured)

Plasma Capillary

Venous

Capillary

æ 6.1 (æ 110)

æ 6.1 (æ 110)

æ 7.0 (æ 126)

æ 7.0 (æ 126)

æ 10.0 (æ 180)

æ 11.1 (æ 200)

æ 11.1 (æ 200)

æ 12.2 (æ 220)

< 6.1 (< 110)

< 6.1 (< 110)

< 7.0 (< 126)

< 7.0 (< 126)

æ 6.7 (æ 120) and < 10.0 (< 180)

æ 7.8 (æ 140) and < 11.1 (< 200)

æ 7.8 (æ 140) and < 11.1 (< 200)

æ 8.9 (æ 160) and < 12.2 (< 220)

æ 5.6 (æ 100) and < 6.1 (< 110) < 6.7 (< 120)

æ 5.6 (æ 100) and < 6.1 (< 100) < 7.8 (< 140)

æ 6.1 (æ 110) and < 7.0 (< 126) < 7.8 (< 140)

æ 6.1 (æ 110) and < 7.0 (< 126) < 8.9 (< 160)

For epidemiological or population screening purposes, the fasting of 2 h value after 75 g oral glucose may be used alone. Glucose concentrations should not be determined on serum unless red cells are immediately removed, otherwise glycolysis will result in an unpredictable underestimation of the true concentrations. Glucose preservatives do not totally prevent glycolysis. If whole blood glucose is used, the sample should be kept at 0± 4  C or centrifuged immediately, or assayed immediately.

practice, an OGTT is rarely required to make a diagnosis of Type 1 diabetes.

NEW CRITERIA IN DIAGNOSTIC VALUE FOR FASTING PLASMA BLOOD GLUCOSE CONCENTRATIONS The main change in the diagnostic criteria for diabetes proposed by both the American Diabetes Association (ADA) and the World Health Organization (WHO) from their previous identical recommendation is the lowering of the diagnostic value of the fasting plasma glucose concentration to 7.0 mmol=l (120 mg=dl) from the former level of 7.8 mmol=l (140 mg=dl) and above. For whole blood the proposed new level is 6.1 mmol=l (110 mg=dl) and above, from the former 6.7 mmol=l (120 mg=dl). The recommended criteria in Table 3.2 allow a diagnosis of diabetes on the basis of an elevation of the 2 h blood glucose (2 h BG) (alone or with the fasting value in the `true' overnight fasting state) provided there is confirmation. When the

FBG meets the diagnostic criteria for diabetes, it is unusual for the 2 h BG to fail to do so (9). However, the converse is not true. Several investigators have found that a large number of subjects meeting the 2 h BG criterion had a normal FBG (10). The new fasting criterion is chosen to represent a value which, in most persons, is of approximately equal diagnostic significance to that of the 2 h post-load concentration, which remains the same. This equivalence has been established from several population-based studies (4, 11 ±13) and it also represents an optimal cut-off point to separate the components of bimodal frequency distributions of fasting plasma glucose concentrations seen in several populations. Furthermore, several studies have shown increased risk of microvascular disease in persons with fasting plasma glucose concentrations of 7.0 mmol=l (126 mg=dl) and over (12) and of macrovascular disease in persons with such fasting concentrations, even in those with 2 h values of 7.8 mmol=l (140 mg=dl) (14). Both FBG and 2 h post-load show relative advantages and are complementary when true fasting can be assured.

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EPIDEMIOLOGICAL STUDIES For the purpose of diabetes epidemiology studies, a single 2 h load glucose value after a 75 g oral glucose load after an overnight fast is often adequate since true fasting cannot be assured in certain conditions, and because of the strong correlation between fasting and 2 h values, epidemiological studies or diagnostic screening have in the past been restricted to the 2 h values only (Table 3.2). OGTT may be difficult to perform for various reasons, e.g. logistic, economic, etc. In that case fasting plasma glucose alone can be used for epidemiological purposes. However, it should be known that some of the individuals identified by fasting values may be different from those identified by the 2 h values, and that overall prevalence may be somewhat different (15) although not always (11, 16). Both the 2 h and fasting value should be used if possible. CLASSIFICATION OF THE DIABETES MELLITUS SYNDROME AND OTHER CATEGORIES OF GLUCOSE INTOLERANCE Previous Classifications In 1965, a WHO Expert Committee on Diabetes Mellitus published the first WHO report containing a classification of patients according to age of recognized onset (17). Since that time, several pathogenic mechanisms have been described and long-term studies have shown different courses and outcomes of different types of diabetes. Many of the subsequent reclassifications proposed attempted to take into account various aspects of diabetes which, in fact, sometimes reflected the specific interests of particular investigators. A great deal of confusion arose from this and it became quite difficult to construct a simple classification that met all interests. In order to overcome these setbacks and establish a new classification that included all possible forms of diabetes mellitus and glucose intolerance, a revised classification of diabetes was formulated by the National Diabetes Data Group (NDDG) (18). This was reviewed, amended and adopted in the second report of the WHO Expert Committee in 1980 (19) and in a modified form in 1985 (1). The

1980 and 1985 classifications of diabetes and allied categories of glucose intolerance included clinical classes and two statistical risk classes (1, 19). The 1980 Expert Committee proposed two major classes of diabetes mellitus and named them insulin-dependent diabetes mellitus (IDDM) or Type 1, and non-insulin-dependent diabetes mellitus (NIDDM) or Type 2 (19). In the 1985 Study Group Report the terms Type 1 and Type 2 were omitted, but the classes IDDM and NIDDM were retained and a new class of malnutritionrelated diabetes mellitus (MRDM) was introduced (1). The 1985 WHO classification was essentially based on clinical descriptions (e.g. insulindependent, non-insulin-dependent, gestational) and did not include terms that might indicate etiological mechanisms (such as Type 1 or Type 2). The question whether certain clinical forms of diabetes (such as the so called `tropical diabetes') were given adequate priority to correct hierarchic order that was raised many years before probably led to the introduction of MRDM although more precise epidemiological data and a better assessment were needed, and called for. Both the 1980 and 1985 reports included other types of diabetes and impaired glucose tolerance (IGT) as well as gestational diabetes mellitus (GDM). The 1985 classification was widely accepted, is used internationally, and represents a compromise between clinical and etiological classifications. Furthermore, it allows classification of individual subjects and patients in a clinically useful manner even when the specific cause of etiology is unknown. The newly proposed WHO and ADA classifications or staging of diabetes still include clinical descriptive criteria but a complementary classification according to etiology is not recommended by either organization (1, 3). New Classifications The ADA classification and the proposed WHO classification encompass both clinical stages, etiological types of diabetes mellitus and other categories of hyperglycemia (Table 3.3). Diabetes may progress through several clinical stages during its natural history regardless of its etiology. The clinical staging reflects this specific aspect. Moreover, individual subjects may move from stage to stage in either direction (Figure 3.2). Even in the

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DIABETES MELLITUS: DIAGNOSIS AND CLASSIFICATION

15

Table 3.3 Etiological classification of diabetes mellitus I.

Type 1 diabetes * ( -cell destruction, usually leading to absolute insulin deficiency) A. Immune mediated B. Idiopathic II. Type 2 diabetes * (may range from predominantly insulin resistance with relative insulin deficiency to a predominantly secretory defect with insulin resistance) III. Other specific types A. Genetic defects of -cell function 1. Chromosome 20, HNF-4 (MODY1) 2. Chromosome 7, glucokinase (MODY2) 3. Chromosome 12, HNF-1 (MODY3) 4. Chromosome 13, IPF-1 (MODY4) 5. Mitochondrial DNA 3243 mutation 6. Others B. Genetic defects in insulin action 1. Type A insulin resistance 2. Leprechaunism 3. Rabson ±Mendenhall syndrome 4. Lipoatrophic diabetes 5. Others C. Diseases of the exocrine pancreas 1. Fibrocalculous pancreatopathy 2. Pancreatitis 3. Trauma=pancreatectomy 4. Neoplasia 5. Cystic fibrosis 6. Hemochromatosis 7. Others D. Endocrinopathies 1. Cushing's syndrome 2. Acromegaly 3. Pheochromocytoma 4. Glucagonoma 5. Hyperthyroidism 6. Somatostinoma 7. Aldosteronoma 8. Others E. Drug- or chemical-induced 1. Nicotinic acid 2. Glucocorticoids 3. Thyroid hormone 4. -adrenergic agonists 5. -adrenergic agonists 6. Thiazides 7. Dilantin 8. Pentamidine 9. Vacor 10. -interferon therapy 11. Diazoxide 12. Others F. Infections 1. Congenital rubella 2. Cytomegalovirus 3. Others G. Uncommon forms of immune-mediated diabetes 1. Insulin autoimmune syndrome (antibodies to insulin) 2. Anti-insulin receptor antibodies 3. `Stiff-man' syndrome 4. Others H. Other genetic syndromes sometimes associated with diabetes 1. Down's syndrome 2. Klinefelter's syndrome 3. Turner's syndrome 4. Wolfram's syndrome 5. Friedreich's ataxia 6. Huntington's chorea 7. Laurence ± Moon ± Biedl syndrome 8. Myotonic dystrophy 9. Porphyria 10. Prader ± Willi syndrome 11. Others IV. Gestational diabetes mellitus (GDM) *Patients with any form of diabetes may require insulin treatment at some stage of their disease. Such use of insulin does not, of itself, classify the patient.

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

absence of information concerning the underlying etiology, persons with diabetes or those who are developing the disease can be categorized by stage according to clinical characteristics. The classification by etiological type results from improved understanding of the cases of diabetes. The new classification takes into account the various degrees of hyperglycemia in individual subjects with any of the disease processes which may lead to diabetes. These are glycemic stages ranging from normoglycemia (normal glucose tolerance) to hyperglycemia (established diabetes where insulin is requested for survival). All individuals with the disease can be categorized according to clinical stage and this is achievable in all circumstances (2). The stage of glycemia may change over time depending on the extent of the underlying disease processes (Figure 3.2). As shown in Figure 3.2 a disease process may be present but may not have progressed far enough to cause hyperglycemia. The etiological classification reflects the fact that the defect or process which may lead to a manifest disease, diabetes, may be identifiable at any stage in the development of diabetes even at the stage of normoglycemia. For instance, the presence of islet cell antibodies in a normoglycemic individual makes it likely that individual has the Type 1 autoimmune process.

For Type 2 diabetes, there are not many good highly specific indicators. Future research will probably reveal some of them. The same disease process can cause various degrees of `dysglycemia' such as impaired fasting glycemia (IFG) and=or impaired glucose tolerance (IGT) without fulfilling the criteria for the diagnosis of diabetes (2). Weight reduction, exercise and=or oral agents treatment can result in adequate glycemic control in some persons with diabetes. These persons, therefore, do not require insulin. Other persons require insulin for adequate glycemic control but can survive without it. By definition these persons have some residual insulin secretion. Patients with extensive beta-cell destruction (no residual insulin secretion) do require insulin for survival. The severity of the metabolic abnormality can either regress (e.g., with weight reduction), progress (e.g., with weight gain) or stay the same. CHANGES IN TERMINOLOGY Both ADA and the proposed WHO classification have eliminated the terms `insulin-dependent diabetes mellitus' and `non insulin-dependent diabetes mellitus' and their acronyms `IDDM' and `NIDDM' on the basis that these terms have

Figure 3.2 Disorders of glycemia: etiological types and clinical stages. * In rare instances, patients in these categories (e.g.: Vacor toxicity, Type 1 presenting in pregnancy) may require insulin for survival

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DIABETES MELLITUS: DIAGNOSIS AND CLASSIFICATION

been confusing and frequently resulted in misclassification, patients being classified on treatment rather than on pathogenesis (Table 3.3). The terms `Type 1 and Type 2' are retained (using Arabic rather than Roman numerals). People with any form of diabetes may require insulin treatment at some stage of their disease. The etiological type named Type 1 encompasses the majority of cases which are primarily due to pancreatic islet beta-cell destruction and are prone to ketoacidosis. Type 1 includes those cases attributable to an autoimmune process, as well as those with beta-cell destruction and which are prone to ketoacidosis for which neither an etiology nor a pathogenesis is known (idiopathic). Those forms of beta-cell destruction or failure to which specific causes can be assigned are not included in this type of diabetes (e.g., cystic fibrosis, mitochondrial defects). The form named Type 2 includes the common major form of diabetes which results from defect(s) in insulin secretion, almost always with a major contribution from insulin resistance. The class malnutrition-related diabetes (MRDM) has been deleted in the proposed WHO classification. The former subtype of MRDM, protein-deficient pancreatic diabetes (PDPD or PDDM) needs more studies for a better definition. The other former subtype of MRDM, fibrocalculous pancreatic diabetes (FCPD), is now classified as a disease of the exocrine pancreas, fibrocalculous pancreatopathy which may lead to diabetes. Chapter 18 on MRDM in this book discusses this issue. The class impaired glucose tolerance (IGT) is reclassified as a stage of impaired glucose regula-

tion (Table 3.4), since it can be observed in any hyperglycemic disorder, and is itself not diabetes. Gestational diabetes is retained but now encompasses the groups formerly classified as gestational impaired glucose tolerance (GIGT) and gestational diabetes mellitus (GDM) according to the new proposed WHO criteria (2). CLINICAL CLASSIFICATION OF DIABETES MELLITUS AND OTHER CATEGORIES OF GLUCOSE TOLERANCE Table 3.3 summarizes the etiological classification of diabetes mellitus. Etiological and clinical stages are presented in Figure 3.2. The concepts for new staging=etiological classification were proposed by Kuzuya and Matsuda (20). Their proposals sought to separate clearly the criteria related to etiology and those related to degree of deficiency of insulin or insulin action and to define each patient on the basis of these two criteria. The newly suggested WHO classification and the new ADA classification bring in both clinical stages of hyperglycemia as well as etiological types (Figure 3.2 and Table 3.3). The classification by etiological type results from new knowledge as to the causes of hyperglycemia including diabetes. The actual staging proposed reflects that any etiological type of diabetes can pass or progress through several clinical phases (both asymptomatic and symptomatic) during its natural history. Moreover individuals may move (in either direction), from stage to stage (Figure 3.2).

Table 3.4. Glucose levels for diagnosis of glucose tolerance abnormalities Category

FPG mmol=l

(mg=dl)

PG 1 h after 75 g glucose load mmol=l

Impaired fasting glucose (IFG) Impaired glucose tolerance (IGT) Diabetes mellitus (DM) Gestational diabetes * (GM)

(mg=dl)

PG 2 h after 75 g glucose load mmol=l

(mg=dl)

6.1± 6.9

(10±124)

N=A

N=A

7.0 mmol=l (126 mg=dl) or a casual plasma glucose >11.1 mmol=l (200 mg=dl) meets the threshold for the diagnosis of diabetes, if confirmed on a subsequent day, and precludes the need for any glucose challenge. In the absence of this degree of hyperglycemia, according to the ADA, evaluation for GDM in women with average or high-risk characteristics should follow one of the two approaches (3). One-step OGTT without prior plasma or serum approach: glucose screening.

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

CONCLUSIONS The most substantive change in diagnostic criteria for glucose intolerance is that the fasting plasma glucose (FPG) concentration for the diagnosis of diabetes has been lowered from 7.8 mmol=l (140 mg=dl) to 7 mmol=l (126 mg=dl). A new category of impaired FPG of 6.1 to 7.0 mmol=l (111 mg=dl to 126 mg=dL) has been created as the ADA recommended abolition of the oral glucose tolerance test (OGTT). This suggestion has not been supported by the 1999 WHO report (2). The OGTT is not used very often to diagnose diabetes in a clinical setting and has been mainly used for clinical research and epidemiological studies. Diabetes can usually be diagnosed without an OGTT, but this is not the case for IGT. While in many settings, the logistics and costs of measuring glycated hemoglobin are less than those of obtaining fasting blood or performing an OGTT, the current disadvantage of glycated hemoglobin is the lack of standardization of methodology as well as the fact there is no universal reference standard for interlaboratory calibration (2). In addition, there are fewer outcome data available than for the OGTT. However, these limitations may be overcome in the near future, so that further evaluation of the properties of HbA1c measurements for screening and diagnosis could justify postponing a change in screening recommendations. There are several arguments for abolishing the OGTT as a routine screening test for Type 2 diabetes. First, the complexity of the current diagnostic criteria reflects both the difficulty in distinguishing diabetic from non-diabetic patients on the basis of a single measurement, and the considerable test=retest variability of the OGTT. However, a major argument for continuing the OGTT relates to the identification of high-risk subjects, i.e. those with IGT for clinical trials of Type 2 diabetes prevention. In addition, the 2 h plasma glucose value from the OGTT was in particular recommended by WHO for epidemiological studies, to overcome uncertainties about whether study subjects were fasting or not. Blood glucose is a continuum, and therefore the choice of a distinct cutpoint will always be somewhat arbitrary. The determination of diagnostic cutpoints which gave rise to the NDDG and

WHO recommendations was based on studies performed which evaluated the association between 2 h plasma glucose and the subsequent development of the microvascular complications of diabetes. The diagnostic cutpoint of 11.1 mmol=l for the 2 h plasma glucose concentration was originally adopted for two reasons. First, the bimodality of glucose distributions in populations with high prevalence of diabetes suggested that 11.1 mmol=l represented the cutpoint separating the two components of the bimodal frequency distributions. Second, when the prevalence of microvascular complications was plotted against the 2 h plasma glucose it became obvious that the former sharply increased at about 11.1 mmol=l. Using the WHO cutpoint values to define Type 2 diabetes, it became apparent that FPG and 2 h plasma glucose detect different sectors of the hyperglycemic state. The 1985 WHO FPG criterion for diabetes (7.8 mmol=l or 140 mg=dl) represents a greater degree of hyperglycemia than the 2 h plasma glucose criteron for diabetes (11.1 mmol=l or 200 mg=dl). The effect of the change will have variable, but not great effects on diabetes prevalence in most populations. The new World Health Organization=ADA recommendations will be welcomed as a basis on which to build. They are based on the accumulated work of many researchers. The rapid advances in molecular biology in the last decade have provided the means to extend our knowledge of the basis for the metabolic and clinical heterogeneity of diabetes. The classification should provide a more rational platform for phenotyping and choosing appropriate therapies for persons with diabetes. Inevitably, the classification and criteria will need to be revised in future years as new evidence-based data emerge. ANNEX 1 THE ORAL GLUCOSE TOLERANCE The oral glucose tolerance test (OGTT) is principally used for diagnosis when blood glucose levels are equivocal, during pregnancy, or in an epidemiological setting to screen for diabetes and impaired glucose tolerance. The OGTT should be administered in the morning after at least 3 days of unrestricted diet

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(greater than 150 g of carbohydrate daily) and usual physical activity. The test should be preceded by an overnight fast of 8± 14 h, during which water may be drunk. Smoking is not permitted during the test. The presence of factors that influence interpretation of the results of the test must be recorded (e.g.: medications, inactivity, infection). After collection of the fasting blood sample, the subject should drink 75 g of anhydrous glucose (or partial hydrolysates of starch of the equivalent carbohydrate content) in 250 ±300 ml of water over the course of 5 minutes. For children, the test load should be 1.75 g of glucose per kg body weight up to a total of 75 g of glucose. Blood samples must be collected 2 h after the test load. Unless the glucose concentration can be determined immediately, the blood sample should be collected in a tube containing sodium fluoride (6 mg per ml whole blood) and immediately centrifuged to separate the plasma; the plasma should be frozen until the glucose concentration can be estimated. For interpretation of results, refer to Tables 3.2 and 3.5) ANNEX 2 METHODS FOR MEASURING SUBSTANCES IN BLOOD AND URINE Measurement of Glucose in Blood Reductiometric methods (the Somogyi ±Nelson, the ferricyanide and neocuprine autoanalyser methods) are still in use for blood glucose measurement. The o-toluidine method also remains in use but enzyme-based methods are widely available, for both laboratory and near-patient use. Highly accurate and rapid (1 ±2 min) devices are now available based on immobilized glucose oxidase electrodes. Hexokinase and glucose dehydrogenase methods are used for reference. Whole blood samples preserved with fluoride show an initial rapid fall in glucose of up to 10% at room temperature, but subsequent decline is slow; centrifugation prevents the initial fall. Whole blood glucose values are 15% lower than corresponding plasma values in patients with a normal hematocrit reading, and arterial values are about 7% higher than corresponding venous values. The use of reagent-strip glucose oxidase methods has made bedside estimation of blood glucose very

25

popular. However, the cost of the reagent-strips remains high. Some methods still require punctilious technique, accurate timing, and storage of strips in airtight containers. Reasonably quantitative results can be obtained even with visual colormatching techniques. Electrochemical and reflectance meters can give coefficients of variation of well under 5%. Reagent-strip methods have been validated under tropical conditions, but are sensitive to extreme climatic conditions. Diabetes may be strongly suspected from the results of reagentstrip glucose estimation, but the diagnosis cannot be confidently excluded by the use of this method. Confirmation of diagnosis requires estimation by laboratory methods. Patients can easily collect small blood samples themselves (either in specially prepared plastic or glass capillary tubes or on filter-paper), and selfmonitoring using glucose reagent-strips with direct color-matching or meters is now widely practiced. Patients should be properly trained in the appropriate techniques to avoid inaccurate or misleading results. The insulin-treated patient is commonly requested to build up a `glycemic profile' by selfmeasurement of blood glucose at specific times of the day (and night). A `7-point profile' is useful, with samples taken before and 90 min after breakfast, before and 90 min after lunch, before and 90 min after an evening meal, and just before going to bed. Occasionally patients may arrange to wake at 03 h to collect and measure a nocturnal sample. The complete profile rarely needs to be collected within a single 24 h period, and it may be compiled from samples collected at different times over several days. Measurement of Glucose in Urine Insulin-treated patients who do not have access to facilities for self-measurement of blood glucose should test urine samples passed after rising, before main meals, and before going to bed. Patients with Type 2 diabetes do not need to monitor their urine so frequently. Urine tests are of somewhat limited value, however, because of the great variation in urine glucose concentration for given levels of blood glucose. The correlation between blood and urine glucose may be improved a little by collecting short-term fractions

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

(15 ± 30 min) or the urine output. Benedict's quantitative solution or self-boiling, caustic soda=copper sulphate tablets may be used or the more convenient, but costly, semi-quantitative enzyme-based test-strips. Ketone Bodies in Urine and Blood The appearance of persistent ketonuria associated with hyperglycemia or high levels of glycosuria in the diabetic patient points to an unacceptably severe level of metabolic disturbance and indicates an urgent need for corrective action. The patient should be advised to test for ketone bodies (acetone and aceto-acetic acid) when tests for glucose are repeatedly positive, or when there is substantial disturbance of health, particularly with infections. Rothera's sodium nitroprusside test may be used or, alternatively reagent-strips that are sensitive to ketones. In emergency situations such as diabetic ketoacidosis, a greatly raised concentration of plasma ketones can be detected with a reagent-strip and roughly quantified by serial 1 in 2 dilution of plasma with water. REFERENCES 1. World Health Organization. Diabetes Mellitus: Report of a WHO Study Group. Technical Report Series 727. Geneva, WHO, 1985. 2. World Health Organization. Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications. Report of a WHO Consultation, Part 1: Diagnosis and Classification of Diabetes Mellitus. Geneva, WHO, 1999. 3. The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care (2000); 23, (suppl. 1): 54 ± 519. 4. Barker DJP, Gardner MJ, Power C. Incidence of diabetes amongst people aged 18 ±50 years in nine British towns: a collaborative study. Diabetologia (1982); 22: 421± 425. 5. Watkins PJ. A new look at diagnostic criteria. Diabetologia (1979); 17: 127± 128. 6. Conn JW. Interpretation of the glucose tolerance test: the necessity of a standard preparatory diet. Am J Med Sci (1940); 199: 555± 564. 7. Conn JW, and Fajans SS. The prediabetic state. Am J Med (1961); 31: 839± 850.

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79. Pak CY, Eun H, McArthur RG, Yoon J. Association of cytomegalovirus infection with autoimmune Type 1 diabetes. Lancet (1988); ii: 1 ± 4. 80. Hirata Y, Ishizu H, Ouchi N et al. Insulin autoimmunity in a case of spontaneous hypoglycaemia. J Jpn Diabet Soc (1970); 13: 312± 320. 81. Bodansky HJ, Grant PJ, Dean BM, McNally J, Bottazzo GF, Hambling MH et al. Islet-cell antibodies and insulin autoantibodies in association with common viral infections. Lancet (1986); ii: 1351± 1353. 82. Solimena M, De Camilli P. Autoimmunity to glutamic acid decarboxylase (GAD) in Stiff-Man syndrome and insulin-dependent diabetes mellitus. Trends Neurosci (1991); 14: 452±457. 83. Fabris P, Betterle C, Floreani A, Greggio NA, de Lazzari F, Naccarato R et al. Development of Type 1 diabetes mellitus during interferon alfa therapy for chronic HCV hepatitis (Letter). Lancet (1992); 340: 548±548.

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84. Flier JS. Lilly Lecture: syndromes of insulin resistance: from patient to gene and back again. Diabetes (1992); 41: 1207± 1219. 85. Khan CR, Baird KL, Flier JS, Jarrett DB. Effects of autoantibodies to the insulin receptor on isolated adipocytes. J Clin Invest (1977); 60: 1094 ±1106. 86. Tsokos GC, Gorden P, Antonovych T, Wilson CB, Balow JE. Lupus nephritis and other autoimmune features in patients with diabetes mellitus due to autoantibody to insulin receptors. Ann Intern Med (1985); 102: 176±181. 87. Barrett TG, Bundey SE, Macleod AF. Neurodegeneration and diabetes: UK nationwide study of Wolfram (DIDMOAD) syndrome. Lancet (1995); 346; 1458± 1463. 88. O'Sullivan JB, Mahan CM. Criteria for the oral glucose tolerance test in pregnancy. Diabetes (1964); 13: 278± 285. 89. Carpenter MW, Coustan DR. Criteria for screening tests for gestational diabetes. Am J Obstet Gynecol (1982); 144: 768±773.

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Prevention of Type 1 Diabetes Mellitus Jay S. Skyler, Alberto Pugliese, Carlos Bernal, and Jennifer B. Marks University of Miami School of Medicine. Miami, Florida, USA

INTRODUCTION The ultimate goal of understanding the epidemiology of any disease is to use that information in the development of programs aimed to prevent or eradicate the disease in question. Such is certainly the case for Type 1 diabetes mellitus. Progress in our understanding of the epidemiology of Type 1 diabetes, including its etiology, genetic and nongenetic influences, changing incidence and prevalence, is reviewed in other chapters in this volume. These factors will be discussed here only as they relate to the purpose of this chapter, the case for implementation of programs for prediction and prevention of Type 1 diabetes. The clinical symptoms of Type 1 diabetes are the overt expression of an insidious pathogenetic process which began years earlier. The consequence of immune-mediated destruction of the pancreatic islet insulin-secreting beta-cells, clinical disease becomes apparent only when a majority of beta-cells have been destroyed. The pathogenesis of Type 1 diabetes is generally thought to involve genetic predisposition to the disease, non-genetic (environmental) factors that appear to act as triggers in genetically susceptible individuals, activating immune mechanisms specifically targeted against pancreatic islet beta-cells. This may be an oversimplification. Rather, the pathogenesis of type 1 diabetes appears to involve a disruption of balance between forces propelling the progression of disease and forces retarding or preventing that progression (Figure 4A.1). This delicate balance appears to be in place for genetic factors, environmental factors, and immune regulation. Thus, there have been identified genes that confer susceptibility or predisposition to the disease, and genes that confer protection against development of the disease. Likewise, there are apparent

environmental insults which have the potential of triggering development of disease in genetically susceptible individuals, while other environmental factors appear to be associated with protection from development of disease. In addition, there seem to be complex regulatory interactions amongst various elements of the immune response, with some elements of the immune circuitry being responsible for beta-cell destruction and others regulating that response and leading to beta-cell protection. DETERMINANTS Genetic There are both genetic and environmental determinants of the Type 1 diabetes disease process. The major genetic susceptibility locus for Type 1 diabetes, IDDM1, is localized within the HLA (human leukocyte antigen) region on the short arm of chromosome 6 (1). IDDM1 provides at least 50% of the genetic susceptibility to Type 1 diabetes (1, 2). The susceptibility within the IDDM1 locus is mostly conferred by alleles of the HLA-DQ locus in the HLA class II region (3). However, a number of studies have shown that alleles at the DRB1 locus are also important and significantly modulate diabetes susceptibility. The HLA molecules DQA1*0301-DQB1 *0302 (also known as DQ8), DQA1 *0501-DQB1 *0201 (also known as DQ2), DRA-DRB1 *0401 (also known as DR4) and DRA-DRB1 *0301 (also known as DR3) confer susceptibility to Type 1 diabetes. A second gene, termed IDDM2, has been mapped to the VNTR (variable number of tandem repeats) minisatellite locus at the 5 0 end of the insulin gene on the short arm of chromosome

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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Figure 4A.1 Elements in the pathogenesis of Type 1 diabetes. Type 1 diabetes emerges when the disease-promoting elements in the left-hand column outweigh the protective elements in the right-hand column, i.e. when the balance of forces favors processes which eventuate in immune destruction of islet beta-cells

11, and accounts for about 10% of the genetic predisposition (4). At least 13 other minor loci have been discovered mostly through microsatellite typing and linkage analysis in large collections of diabetic families with affected sib-pairs, and may contribute to diabetes risk (5). The best characterized loci, IDDM1 (HLA) and IDDM2 (INS-VNTR), encode true susceptibility genes. Yet, there is also clear evidence that certain alleles provide genetic resistance from the development of diabetes. In particular, genetic protection from Type 1 diabetes is associated with specific alleles at the IDDM1 (6) and IDDM2 loci (7, 8). For example, the HLA molecule DQA1*0102DQB1*0602 (also known as DQ6). There usually is dominance of protection over susceptibility for genes encoded at these loci. It is a fair speculation that IDDM1 may be involved in antigen presentation and control immune responsiveness to one or more islet cell antigens, while IDDM2 may control insulin gene expression in the thymus and in turn selectively influence immune responsiveness to insulin. These two susceptibility loci may influence the specificity of the autoimmune response rather than a generic predisposition to autoimmunity. That genetics plays an important role in the development of Type 1 diabetes has been demonstrated from a number of careful studies. There is a

higher concordance rate for Type 1 diabetes in monozygotic twins (35 ±50%) than in dizygotic twins (5 ± 10%) (9). In siblings of probands of Europoids with Type 1 diabetes, the risk is 6% (10), while offspring of diabetic parents have a 3% risk if the mother has the disease, and a 6% risk if the father has the disease (11). The cumulative concordance or recurrence risk of Type 1 diabetes up to the age of 40 years in dizygotic twin pairs is twice as high as in ordinary first-degree relatives of patients up to the same age (12). The much higher concordance rate for diabetes among monozygotic than dizygotic twins implies that there is an inherited component to a disease. Yet, the substantial (50 ±65%) discordance rate in monozygotic twins indicates that environmental factors also must play a role in human Type 1 diabetes. In contrast to the familial predisposition noted above, in the general population, the risk is much less. For example in the United States amongst Europoids the overall risk is 0.2 to 0.4%. However, there is considerable racial and ethnic variation in Type 1 diabetes incidence (e.g. 3.3 per 100 000 in African Americans in San Diego, CA, to 20.6 per 100 000 in whites in Rochester, MN), and about 40% of the incidence rate variation in the United States can be explained by racial composition (13).

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A recent study examined the frequency of various islet cell antibodiesÐislet cytoplasmic antibodies (ICA), insulin autoantibodies (IAA), and glutamic acid decarboxylase antibodies (GADA) in 18 monozygotic and 36 dizygotic twin pairs with one or both partners having Type 1 diabetes (14). There was no difference between the prevalence of these three islet cell autoantibodies in dizygotic (26%, 49%, and 40%) and monozygotic (20%, 50%, and 40%) twins without diabetes, suggesting that islet cell autoimmunity is environmentally rather than genetically determined. Moreover, the prevalence of these antibodies was higher in the non-diabetic monozygotic and dizygotic twins than in other first degree relatives (4%, 3%, and 4%) of patients with Type 1 diabetes (12). This implies that there is etiological importance to the prenatal or early postnatal period during which twins are exposed to the same environment, in contrast with that experienced by first-degree relatives. Environmental Many factors suggest that environmental factors are important determinants of Type 1 diabetes. It has already been noted that environmental factors are implicated by the discordance rate in monozygotic twins and the higher prevalence of autoimmunity in non-diabetic twins than in other firstdegree relatives. Other indicators that environmental factors are involved include the seasonal variation in disease onset (15) and the rising incidence of Type 1 diabetes in Europe and many other parts of the world over the past 20 ±30 years (16, 17). Although in most areas, there has been an overall increase in incidence rates, in two studiesÐ one in Finland and one in England Ð there has been a marked increase amongst children aged under 5 years (18, 19). The rapid changes in incidence seen in populations that are for the most part genetically stable suggests a major role for environmental factors encountered early in life, e.g. before birth or in early postnatal life. Viral Infection Viruses have long been implicated as possible environmental determinants in Type 1 diabetes.

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Direct effects of viruses, causing beta-cell destruction, are possible (20). In one dramatic case, a Coxsackie-B variant was isolated from pancreatic tissue in a young boy who succumbed within 10 days of the onset of Type 1 diabetes, and the viral isolate produced diabetes in experimental animals (21). In two other cases of death in young children shortly after diabetes onset, there was demonstration of superantigen involvement (22), which suggests acute viral infection. Yet, these examples of direct effects of viruses are exceedingly rare, and most likely do not account for many cases of Type 1 diabetes. On the other hand, there is evidence that maternal viral infection during pregnancy is a risk factor for childhood-onset Type 1 diabetes. As many as 10 ±12% of children with congenital rubella develop Type 1 diabetes (23, 24). The patients who develop Type 1 diabetes have the typical genetic background, and manifest the usual immunologic abnormalities (25). Congenital cytomegalovirus infection has also been implicated in Type 1 diabetes (26). Careful studies from Sweden and Finland, using maternal cord blood, have found that maternal enteroviral infection during pregnancy, especially Coxsackie-B virus infection (but also echo virus), is a risk factor for Type 1 diabetes (27, 28, 29). Thus, children of mothers who expressed viral antibodies at delivery are at increased risk for developing childhood onset Type 1 diabetes. A fetal viral infection may initiate autoimmunity or cause persistent infection that may lead to progressive beta-cell destruction. One mechanism by which viral infection may influence the immune response is through molecular mimicry. There is a homologous domain in the Coxsackie-B virus protein 2C (p. 2C) and the islet autoantigen glutamic acid decarboxylase (GAD) (30). There is T-cell reactivity to GAD peptide sequences shared with Coxsackie virus protein in recent-onset Type 1 diabetes (31). Moreover, it has recently been shown that this homologous domain is highly conserved in the Coxsackie B-like enteroviruses (32). These are very prevalent enteroviruses and therefore exposure to the mimicry motif will be a frequent event throughout life. It has also been shown that the p. 2C domain binds to the diabetes associated HLA-DR3 molecule (32). It is thus possible that this molecular mimicry may be limited to the HLA-DR3 positive subpopulation of Type 1 diabetic patients.

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There are other examples of possible molecular mimicry involving viruses and islet antigens. These include a homologous domain in insulin and a retrovirus sequence (33), a homologous domain in an islet tyrosine phosphatase IA2 and a rotavirus sequence (34), a homologous domain in a 52 kD islet protein and a rubella protein (35), and a homologous domain in a 38 kD islet protein and cytomegalovirus (36). The are other mechanisms, too, by which viral infections may induce immune reactions, and lead to beta-cell destruction (37). These include induction of expression of HLA class II molecules with subsequent antigen presentation, retrovirus or slow virus infections, or alteration of T-cell receptors. Viral infections also may serve as a stress factor, bringing Type 1 diabetes to clinical recognition. In a nationwide incident case control study in Sweden, the odds ratios for Type 1 diabetes for children exposed to 0, 1 ± 2, or over 2 infections during the last year before diagnosis of diabetes revealed a linear increase (OR = 1.0, 1.96 and 2.55 for 0, 1 ± 2 and over 2 infections, respectively), which remained significant when standardized for possible confounders such as age and sex of the children, maternal age and education and intake of antibiotics and analgesics (38). This study also found a protective effect of measles vaccination. Chemical Toxins Several chemical toxins have been shown to have the potential of destroying beta-cells. Amongst these are the nitrosourea compounds, including the drug stretozotocin, the rotenticide Vacor (N-3pyridil-methyl-N-p-nitrophenyl urea) (39) and other nitrosourea compounds (40). Unfortunately, such compounds are ubiquitous in our environment, and represent only one class of chemical compounds that may have the potential of leading to Type 1 diabetes. Another group of potentially toxic substances is the nitrates and nitrites. One study suggested that maternal consumption of nitrates and nitrites around the time of conception may influence the eventual development of Type 1 diabetes (41). In this case the nitrates and nitrites were contained in smoked mutton, consumed

disproportionately as part of holiday festivities. The notion is that a developing embryo, when exposed to these toxins, suffers an initial beta-cell insult, which enhances the risk of diabetes, appearing years later. Similar studies in animals support this interpretation (42, 43). In the Swedish a nationwide study, a high intake of foods rich in nitrosamine conferred risk, and the frequency of infections and a high nitrosamine intake tended to interact, indicating a synergistic effect with an odds ratio of 11.8 (44). Neonatal Nutrition One potential environmental influence is neonatal nutrition. It has been proposed that consumption of cow milk proteins may increase susceptibility to Type 1 diabetes (45). A number of studies have examined the relationships amongst breast milk feeding, introduction of cow milk proteins, and the frequency of Type 1 diabetes, with conflicting results (46 ± 48). A meta-analysis has suggested both that exclusive breast feeding may be protective and that early cow milk consumption may confer risk (49). A small prospective Finnish study has suggested that exclusive breastfeeding may reduce the likelihood of disease development (50). Although controversial and not reproducible, in one study a group of diabetic children were found to have elevated levels of antibodies directed against bovine serum albumin, which may be the relevant cow's milk protein (51). A suggested mechanism is molecular mimicry between the betacell surface protein ICA-69 and a 17 amino acid sequence of bovine serum albumin (51). These studies have led the investigators to propose that neonatal exposure to cow's milk may lead to the initiation of the immunologic attack against pancreatic islet beta-cells and the development of Type 1 diabetes. As a consequence, they have proposed and initiated a controlled clinical trial in infants who are first-degree relatives of individuals with Type 1 diabetes, in which the treatment group will receive a cow's milk-free-formula and the control group will receive conventional formula containing cow's milk (52). This trial is known as TRIGR, Trial to Reduce IDDM in Genetically at Risk. It plans to enroll several thousand newborns and follow them for up to a decade.

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DISEASE PROCESS The Type 1 diabetes disease process is one of selective destruction of the insulin-producing betacells in the pancreatic islets of Langerhans (53, 54). The current concept is that islet beta-cells are destroyed by an immune response mediated by Tlymphocytes that react specifically to one or more beta-cell proteins (autoantigens). The exact mechanisms have not yet been clearly defined, as there appear to be complex regulatory interactions amongst various elements of the immune response, and it is unclear which elements of the immune circuitry are responsible for beta-cell destruction and which are responsible for beta-cell protection. The prevalent view is that islet cell destruction is enhanced by CD8‡ cytotoxic T-lymphocytes stimulated by T-helper-1 (Th1) subset of CD4‡ Tlymphocytes, with inhibition of islet destruction by T-helper-2 (Th2) subset of CD4‡ T-lymphocytes and CD8  suppressor T-lymphocytes. Thus, the pathogenetic sequence potentially could be altered either by downregulation of destructive forces or by enhancement of protective forces. This explains why both immunosuppressive and immunostimulatory (or immunomodulatory) approaches may be beneficial. Also, there is some debate as to whether Type 1 diabetes is an antigen specific autoimmune disease or an inflammatory disease that arises because beta-cells are inherently less able to withstand local environmental insults than are other cell types. In fact, the development of Type 1 diabetes likely involves collaboration amongst islet cell specific and non-specific mechanisms, including a complex orchestration of the entire immunologic repertoire. Diabetes develops in a highly tissue specific, cytokine-rich environment of insulitis. Immune activation appears to involve presentation (by antigen presenting cells in the context of MHC class II molecules) to the immune system of a diabetogenic peptide. As a consequence of antigen (immunogen) presentation, there is activation of a Th1 subset of CD4‡ T-lymphocytes. The cytokines produced by Th1-cell activation, interleukin-2 (IL-2) and interferon- (IFN- ), activate cytotoxic T-lymphocytes and cytotoxic macrophages to kill islet beta-cells by a variety of mechanisms. These killing mechanisms include oxygen free radicals, nitric oxide, destructive cytokines (interleukin-1 [IL-1], tumor necrosis factor- [TNF-], tumor necrosis

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factor- [TNF- ], and interferon- [IFN- ]), and CD8‡ cytotoxic T-lymphocytes that interact with a beta-cell autoantigen-MHC class I complex. Once the initial immune destruction commences, secondary and tertiary immune responses also are activated, with virtually the whole immunologic army attacking beta-cells. In the process, many beta-cell proteins serve as antigens that generate both cellular and humoral immune responses, thus rendering nearly impossible the task of identifying putative `primary' triggering antigens. Although the diabetogenic peptide is as yet unknown, there are several candidates, including insulin, glutamic acid decarboxylase (GAD), and islet tyrosine phosphatases (IA2 and IA2 ). After initiation of the immune response by an antigen acting as an immunogen, there is an amplification cascade, with both intramolecular epitope (or determinant) spreading and intermolecular antigen spreading. Ultimately, then, several different antigens are immunologic targets and also may be used as immunologic modulators (tolerogens or regulogens). Prevention strategies to be discussed herein are those designed to interrupt the pathogenetic sequence, the Type 1 diabetes disease process. STAGES IN DEVELOPMENT The development and course of Type 1 diabetes can be divided into a number of stages, the earlier of which are depicted in Figure 4A.2. The Type 1 diabetes process in any individual may progress through some or all of them. The purpose of dividing the disease into these stages is to note that interruption of the sequence at any stage, preventing the development of the next stage, is likely to be an important advance. Primary prevention may not be possible, in the strictest sense, particularly if the diseases process is initiated in utero. Stage 1. The first stage is genetic susceptibility, modulated by genetic protection. This stage is identified by finding of susceptibility genes without dominant protective genes. Stage 2. In the second stage, an environmental trigger initiates autoimmunity. This eventuates in an anti-beta-cell cellular immune response leading to an immune mediated islet infiltrate (insulitis), with consequent beta-cell injury, impairment of

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Figure 4A.2 Schematic depiction of the evolution of Type 1 diabetes through stages, as discussed in text

beta-cell function, and some loss of beta-cell mass. As beta-cells are injured, a presumably secondary humoral immune response develops, with the appearance of beta-cell autoantibodies. The principal antibodies are islet cytoplasmic antibodies (ICA), insulin autoantibodies (IAA), glutamic acid decarboxylase antibodies (GADA), and antibodies to islet tyrosine phosphatases (IA2 and IA2 ). (The antibodies to IA2 include the antibody ICA512 directed at a component of IA2, while the antibodies to IA2 include one directed against an insulin granule membrane protein, phogrin [phosphatase homologue of granules from rat insulinoma].) This stage is identified by the presence of autoantibodies. Stage 3. In the third stage, there is sufficient impairment of beta-cell function and=or loss of beta-cell mass to result in loss of first-phase insulin response (FPIR) during an intravenous glucose tolerance test (IVGTT). This stage is identified by loss of FRIP in an IVGTT. Stage 4. In the fourth stage, there is impaired glucose tolerance (IGT) and=or impaired fasting glucose (IFG), but without overt diabetes. This stage is identified by glucose levels either fasting (IFG) or after a glucose challenge (IGT), that are elevated above the upper limit of normal, but have not reached the diagnostic threshold for diabetes. Stage 5. The fifth stage is marked by the clinical onset of Type 1 diabetes. At the beginning of this stage it is estimated that over 80% of beta-cell function and=or mass has been lost, but the residual beta-cell function (evidenced by c-peptide

production) remains an important contributor to metabolic homeostasis. These individuals are identified by hyperglycemia exceeding the diagnostic threshold for diabetes, and by measurement of intact c-peptide secretion. Stage 6. The onset of the sixth stage is marked by loss of all beta-cell function and mass (evidenced by lack of any c-peptide response to provocative challenge), and has been called `total' diabetes. Diabetes becomes more difficult to control. As beta-cell function is lost and `total' diabetes evolves, antibodies tend to decrease in titer and=or disappear. These individuals are identified by fluctuating glycemia and by absence of c-peptide secretion. Stage 7. In the seventh stage, diabetic complications (retinopathy, nephropathy, neuropathy) develop. These individuals are identified by clinical and laboratory abnormalities signifying the presence of these complications. Stage 8. In the eighth stage, one or another diabetic complication has progressed to blindness, renal failure, amputation, or other clinical disability. PREVENTION STRATEGIES Although it is possible to consider intervention at any of these stages, the tertiary intervention needed to forestall complications will not be considered in this chapter. Rather, the focus will be on interrupting the Type 1 diabetes disease

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process. In this regard, there are several possible prevention strategies which could alter the natural history of the disease. These include: 1. Identification and elimination of environmental triggers. This might include eradication of maternal viral infections, perhaps by immunization of mothers prior to conception. It also might include elimination of cow's milk proteins from neonatal formulae. 2. Identification and enhancement of environmental protective factors. This might include promotion of breastfeeding. 3. Interruption of the immunologic sequence leading to beta-cell destruction. This could involve immune modulation that decreases destructive or increases protective forces, tolerization to beta-cell antigens, or development of anergy to these. It could also involve blockage of destructive elements (e.g. cytokines or free radicals) or promotion of beta-cell survival. It is possible to consider using these at various stages of the disease, such as: 1. Immune intervention begun shortly after diagnosis (during Stage 5) when there is still some residual beta-cell function, in an effort to modify the severity of clinical manifestations, halt the destruction of beta-cells, and perhaps allow residual beta-cells to recover their function. 2. Identifying those at risk for the development of diabetes, during the preclinical period (Stages 2 or 3), and designing an intervention which might arrest the immune destruction before it becomes clinically evident, and thus prevent the disease syndrome. In attempting to identify those at risk, to test interventions, much attention has focused on relatives (principally first-degree relatives) of people with Type 1 diabetes. In relatives, case finding is much easier because they have a 10±20-fold increased risk over the general population, making identification of high-risk individuals easier. Yet, even amongst relatives, except for monozygotic twins, the odds of identifying an individual potentially at risk are very small, as only 3±4%

of relatives will have identifiable autoimmunity (12). Yet, a problem is in newly diagnosed Type 1 diabetes, there is a first-degree family history of Type 1 diabetes only in 10±15% of children. For example, in one series from England, 12.8% of children had a first-degree relative with the disease, the relative affected being the father in 4.5%, the mother in 2%, and a sibling in 4.5% (19). It should be noted that all evidence suggests that the pathogenetic sequence is the same in sporadic non-familial cases (55, 56) as it is in relatives (57, 58). Thus, the same interventions would likely apply. The differences are that of case-finding and the need to have screening approaches with sufficiently high positive predictive value to be used in the general population. Amongst relatives, for example, autoantibody screening represents a relatively simple way of initiating the process of risk assessment. On the other hand, the logistics of autoantibody screening, presumably on a repetitive basis, usually would preclude this as the initial step in risk assessment in the general population. Here, two options seem likely: 1. A case-finding strategy, perhaps involving genetic screening at birth, followed by autoantibody screening in those genetically at risk, followed by appropriate intervention. This casefinding approach is being taken in the DAISY (Diabetes Autoimmunity Study in the Young) (59), DIPP (Diabetes Prediction and Prevention Project) (60), and NOBADIA (Norwegian Babies against Diabetes) (61) studies. In such a strategy, treatment (e.g. vaccination) would be only of susceptible individuals. 2. A population strategy. In this scenario, one might have a treatment approach (e.g. vaccination) sufficiently safe and effective that the entire population could be treated. With time, it should be possible to implement one or both of these strategies. The prevention of Type 1 diabetes is a realistic possibility for the future. REFERENCES 1. Davies JL, Kawaguchu Y, Bennet ST, Copeman JB, Cordell HJ et al. A genome-wide search for human Type 1 diabetes susceptibility genes. Nature 1994; 371: 130± 136.

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28. Dahlquist GG, Ivarsson S, Lindberg B, Forsgren M. Maternal enteroviral infection during pregnancy as a risk factor for childhood IDDM: a population-based case-control study. Diabetes (1995); 44: 408± 413. 29. HyoÈty H, Hiltunen M, Knip M, Laskkonen M, VaÈhaÈsalo P, Karjalainen J, Koskela P, Roivainen M, Leinikki P, Hovi T, AÊkerblom HK, and the Childhood Diabetes in Finland (DiMe) Study Group. A prospective study of the role of Coxsackie-B and other enterovirus infections in the pathogenesis of IDDM. Diabetes (1995); 44: 652± 657. 30. Kaufman DL, Erlander MG, Clare-Salzler M, Atkinson MA, Maclaren NK, Tobin AJ. Autoimmunity to two forms of glutamate decarboxylase in insulin-dependent diabetes mellitus. J Clin Invest (1992); 89: 283± 292. 31. Schloot NC, Roep BO, Wegmann DR, Yu L, Wang TB, Eisenbarth GS. T-cell reactivity to GAD65 peptide sequences shared with Coxsackie virus protein in recent-onset IDDM, post-onset IDDM patients and control subjects. Diabetologia (1997); 40: 332±338. 32. Vreugdenhil GR, Geluk A, Ottenhoff THM, Melchers WJG, Roep BO, Galama JMD. Molecular mimicry in diabetes mellitus: the homologous domain in Coxsackie B virus protein 2C and islet autoantigen GAD65 is highly conserved in the Coxsackie B-like enteroviruses and binds to the diabetes associated HLA-DR3 molecule. Diabetologia (1998); 41: 40 ± 46. 33. Hao W, Serreze DV, McCulloch DK, Neifing JL, Palmer JP. Insulin (auto)antibodies from human IDDM cross-react with retroviral antigen p. 73. J Autoimmun (1999); 6: 787± 798. 34. Honeyman MC, Stone NL, Harrison LC. T-cell epitopes in Type 1 diabetes autoantigen tyrosine phosphatase IA-2: potential for mimicry with rotavirus and other environmental agents. Mol Med (1998); 4: 231± 239. 35. Karounos DG, Wolinsky JS, Thomas JW. Monoclonal antibody to rubella virus capsid protein recognizes a beta-cell antigen. J Immunol (1993); 150: 3080± 3085. 36. Pak CY, Fun HM, McArthur RG, Yoon JW. Association of cytomegalovirus infection with autoimmune Type 1 diabetes. Lancet (1988); ii: 1 ± 4. 37. Schattner A, Rager-Zisman B. Virus-induced autoimmunity. Re Infect Dis (1990); 12: 204± 222. 38. Blom L, NystroÈm L, Dahlquist G. The Swedish childhood diabetes study. Vaccinations and infections as risk determinants for diabetes in childhood. Diabetologia (1991); 34:3 176± 181. 39. Karam JH, Lewitt PA, Young CW, Nowlain RE, Frankel BJ, Fujiya H, Freedman ZR, Grodsky GM. Insulinopenic diabetes after rodenticide (Vacor) ingestion. Diabetes (1980): 29: 971± 978. 40. Rayfield EJ, Ishimura K. Environmental factors and insulin dependent diabetes mellitus. Diabetes Metabol Rev (1987); 3: 925± 957.

39

41. Helgason T, Jonasson MR. Evidence for a food additive as a cause of ketosis-prone diabetes. Lancet (1981); 2: 716± 720. 42. Helgason T, Ewen SWB, Ross IS, Stowers JM. Diabetes produced in mice by smoked=cured mutton. Lancet (1982); 2: 1017± 1024. 43. Ewen SWB, Jaffray B, Pollack JRA, Helgason T, Stowers JM. Nitrosamines and the etiology of insulin dependent diabetes mellitus. Diabetic Med (1985); 2: 297. 44. Dahlquist G, Blom L, LoÈnnberg G. The Swedish Childhood Diabetes Study Ð a multivariate analysis of risk determinants for diabetes in different age groups. Diabetologia (1991); 34:10 757± 762. 45. Martin JM, Trink B, Daneman D, Dosch HM, Robinson B. Milk proteins in the etiology of insulin dependent diabetes mellitus (IDDM). Ann Med (1991); 23: 447 ±452. 46. Borch-Johnsen K, Joner G, Mandrup-Paulsen T, Christy M, Zachan-Christiansen B, Kastrup K, Nerup J. Relationship between breastfeeding and incidence rates of insulin dependent diabetes mellitus. Lancet (1984); 2: 1083± 1086. 47. Kostraba JN, Cruickshanks KJ, Lawler-Heavner J, Jobim LF, Rewers MJ, Gay EC, et al. Early exposure to cow's milk and solid foods in infancy, genetic predisposition, and risk of IDDM. Diabetes (1993); 42: 288 ±295. 48. Norris JM, Beaty B, Klingensmith G, Yu Liping, Hoffman M, Chase HP, Erlich HA, Hamman RF, Eisenbarth GS, Rewers M. Lack of association between early exposure to cow's milk protein and beta-cell autoimmunity. Diabetes Autoimmunity Study in the Young (DAISY). J Am Med Assoc (1996); 276:8 609± 614. 49. Gerstein HC. Cow's milk exposure and Type 1 diabetes. Diabetes Care (1994); 17: 13 ±19. 50. Virtanen SM, Rasanen L, Aro A, Lindstrom J, Sippola H, Lounamaa R, Toivanen L, Tuomilehto J, Akerblom HK, Childhood Diabetes in Finland Study Group. Infant feeding in Finnish children 10 mmol=l during the test was arbitrarily designated as diabetic; 3 Aboriginals and 4 whites in this category. All Aboriginals for whom baseline data were available (10=13) lost weight during the study. The most weight was lost by those most overweight, i.e. women. Food was varied and plentiful during study and relied heavily on animal sourcesÐ perhaps because the gun and fishing equipment made these easier to obtain. Æ20% CHO, æ30% fat and 50% protein. In the urban setting the diet was high in fat, alcohol, and refined CHO and low in protein (Æ10%). Caucasians consumed low±mod alcohol, CHO 45±50%, fat 35±40%, protein 10± 15% diet. Aborigines had lower serum cholesterol than whites, which did not fall during study. Triglycerides increased with urban diet and were higher than whites in urban setting. Fasting plasma glucose in Aborigines was lower than in Caucasians, in bush or urban setting, and not changed by lifestyle change. Starch test showed that in urban Aboriginals there was a more immediate increase in plasma glucose than in traditional Aborigines or Whites. Thereafter rates of change were similar. Insulin responses were steepest in urban Aborigines, intermediate in traditional Aborigines and showed no change in first 15 mins for Caucasians. Differences were maintained thereafter and cannot be attributed to relative obesity in Aborigines.

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

are currently under evaluation. Therefore, realistic hopes for primary prevention of Type 2 diabetes in Aboriginal communities remain with populationwide healthy lifestyle programmes. What are needed now are demonstrated effective strategies to bring about changes in diet and physical activity which can reduce rates of Type 2 diabetes and CVD risk factors in Aboriginal communities. These are unlikely to work in isolation from initiatives to improve general living conditions and address the social and political issues confronting Aboriginal people today (6). EXPERIENCES IN OTHER INDIGENOUS POPULATIONS Given the paucity of information on intervention programmes available for Aboriginal communities (6), it is useful to briefly review experiences in similar populations. One of the best documented is that in Native Americans, the Zuni Diabetes Program in New Mexico. This is a communitybased exercise programme initiated in July 1983, and now offering 48 aerobic sessions over 5 days each week (37). Sessions specifically for people with Type 2 diabetes or for the general public are provided. Participants are recruited by personal invitation, recommendations from medical staff and community advertising. Special events are held throughout the year sponsored by community agencies and local businesses. Indian Health Service staff coordinate the program which also employs community members to run the exercise sessions. A prospective evaluation has not been performed to determine whether the programme is reducing the incidence of Type 2 diabetes. However, results from a small sample of Zuni Indians with Type 2 diabetes suggest that the programme successfully facilitated weight loss, with participants losing 4.09  4.9 kg and non-participants losing 0.91  3.9 kg over a 50-week period (37). While the weight lost was not great in magnitude, it was accompanied by a drop in mean fasting plasma glucose concentration of 2.4 mmol=l. The ability of the programme to attract and retain 30 people for a mean attendance of 37 weeks suggests that such programmes can be successful. The authors postulate that some of the characteristics of this programme that have made it successful are

the reinforcement of exercise behaviour through multiple classes at a range of sites, community exposure to the exercise message and the ongoing nature of the programme (37). It may also be that the involvement of community members as group leaders has helped acceptance of the programme. Ethnographic methods were used to assist in the development of a community-based diabetes prevention programme in an isolated Native American community on the Sandy Lake reserve in northern Ontario (38). An understanding of the socio-cultural context of diabetes is essential for prevention of Type 2 diabetes in the Sandy Lake population, and also in other indigenous populations, such as Australian Aborigines. Therefore, answers to questions regarding local terms used to talk about diabetes, perceptions of causes, consequences and treatment of diabetes and how this information can be used in preventive programmes were sought, using ethnographic methods. Diabetes was perceived to be a common problem in the community but it was considered to be of intermediate severity because of its chronic rather than acute nature, i.e. it did not require a trip to the hospital or cause severe pain. The Sandy Lake community identified `bad diet' and eating too much `white man's' food as causes of diabetes but did not link lack of physical activity and obesity directly with diabetes (38). There was less understanding of what made certain foods good or bad in terms of nutrient content. Diabetes was considered a white man's illness which could be treated by eating more bush food and less white man's food. However, despite the consistency of this view, store purchases suggested that this was not commonly done. Socio-cultural Perspectives of the Prevention of Type 2 Diabetes Whatever the views or perceptions of European Australians, Aboriginal people undeniably perceive that progress in overcoming their social, education and health problems will not occur without recognition of their rights to, and access to, land (6). Whilst major High Court decisions have delivered some legal weight to Aboriginal claims and aspirations, the fact that these judgments and their intent remain under question suggests that non-Aboriginal Australians have not

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EVIDENCE FOR PREVENTION: TYPE 2 DIABETES

yet accepted the Aboriginal views linking land and reconciliation to health problems and other areas of inequality. Given the long history of Aboriginal health problems, and the importance that they place on land rights and reconciliation, it seems reasonable to conclude that there will indeed not be substantial improvements until these fundamental issues are resolved (39).

*

*

* *

SUMMARY

*

The evidence for the potential of pharmaceutical interventions to prevent diabetes awaits confirmation from clinical studies in high-risk groups (23, 26). These trials may not be applicable if community-based approaches are recommended in populations with high diabetes prevalence and associated diseases. There are a number of issues to be considered in planning preventive strategies, some of them emerging from cultural differences and the special socio-economic circumstances under which Aborigines and other indigenous people live. Community-based preventive programs and initiatives do exist; however, a critical evaluation of success is missing and outside the scope of this review. There are a number of general issues, which can reduce the success of prevention campaigns, e.g.:

*

*

Psychological factors such as illusion of immortality, which can impact on the commitment for prevention.

Insufficient knowledge about prevention, resulting in apathy of participants and decisionmakers alike. Low priority for research and funding for prevention amongst politicians, academics and institutions. Vested commercial interest in certain aspects of lifestyle and nutrition. Socio-economic factors which can inhibit changes in diet and lifestyle. Economic benefits of curative medicine, which de-emphasizes prevention. Late benefits of prevention, which are often initially intangible, but require upfront financial investment. Support for prevention is therefore difficult to obtain in politics governed by the quest for short-term results.

The demographic, behavioural and environmental causes of Type 2 diabetes are being increasingly well described (1, 3, 11). The specific roles of obesity, lack of physical activity, and, to a lesser extent, nutritional factors such as low-fibre diet, high-saturated fat, etc. and perhaps low birthweight, can explain a significant proportion of the aetiology of Type 2 diabetes (Table 4.4) (3). These findings provide clues for strategies to prevent Type 2 diabetes. The genetics of Type 2 diabetes remain poorly understood, despite major advances in the understanding of the single gene mutations causing MODY (1). Pharmacological compounds, e.g. biguanides, glucosidase inhibitors and thiazolidinediones, may

Table 4.4 Proposed behavioural and environmental determinants of Type 2 diabetes based on findings from cross-sectional or longitudinal studies Determinant

Strength of association

Control for confounding factors

High body mass index Increased central obesity Physical inactivity Excessive intake of * energy * simple carbohydrates * saturated fats * alcohol Low intake of * dietary fibre * certain trace elements Use of some antihypertensive drugs

High High Intermediate

Adequate Adequate Not complete

Intermediate Weak Intermediate Weak

Not Not Not Not

Intermediate Weak Intermediate

Not satisfactory Not satisfactory Not complete

satisfactory satisfactory satisfactory satisfactory

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

prove useful in the prevention of the deterioration from IGT to Type 2 diabetes. However, at least for economic reasons, these are unlikely to be helpful in the developing world in the near future. However, the epidemiological studies that have demonstrated aetiological roles for a number of potentially modifiable factors related to lifestyle, such as physical activity, obesity and diet, hold out some hope of being able to stem the tide. Intervention studies based on modifications of diet and exercise are starting to show promising results, but almost certainly need to be combined with socio-political changes. This seems necessary in order to implement changes of lifestyle outside a clinical trial setting which should persuade large enough numbers of people of different cultures to avoid sedentary lifestyles and to follow healthy diets. At present there are approximately 110 million people with diabetes in the world but this number will reach around 234 million by the year 2010, the majority of them with Type 2 diabetes (2). Thus there is an urgent need to implement strategies to prevent the emerging global epidemic of Type 2 diabetes. Tackling diabetes must be seen as part of an integrated programme that addresses other lifestyle-related disorders. The prevention and control of Type 2 diabetes and the other major non-communicable diseases can be cost- and health-effective through an integrated approach to NCD disease prevention and control. REFERENCES 1. Zimmet P. Diabetes epidemiology as a trigger to diabetes research. Diabetologia (1999); 42: 499± 418. 2. Amos A, McCarty D, Zimmet P. The rising global burden of diabetes and its complications: estimates and projections to the year 2010. Diabetic Med (1997); 14 (suppl 5): S1± 45. 3. De Courten M, Bennett PH, Tuomilehto J, Zimmet P. Epidemiology of NIDDM in non-europids. In: KGMM Alberti, P Zimmet, RA DeFronzo (eds), International Textbook of Diabetes Mellitus, 2nd edn. Chichester; Wiley, 1997: pp. 143± 170. 4. Zimmet P. Challenges in diabetes epidemiology Ð from West to the Rest. Diabetes Care (1992); 15: 232±252. 5. Pan X, Li G, Hu Y et al. Effect of diet and exercise in preventing NIDDM in people with impaired glucose tolerance: The DaQing IGT and Diabetes Study. Diabetes Care (1997); 20: 537± 544.

6. De Courten M, Hodge A, Dowse G et al. Review of Epidemiology, Aetiology, Pathogenesis and Preventability of Diabetes in Aboriginal and Torres Strait Islander Populations. Canberra, Dept Health and Family Services, 1998. 7. World Health Organization Study Group. Diabetes Mellitus. Technical report series no. 727. Geneva, WHO, 1995. 8. World Health Organization: Report of a Consultation. Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications. Part 1: Diagnosis and Classification of Diabetes Mellitus. Geneva, World Health Organization, 1999. 9. Zimmet PZ. The pathogenesis and prevention of diabetes in adults. Diabetes Care (1995); 18: 1050±1064. 10. Zimmet P, Tumer R, McCarty D, Rowley M, Mackay I. Crucial points at diagnosisÐ NIDDM or slow IDDM. Diabetes Care (1999); 22: B59 ± B64. 11. Tuomilehto J, Tuomilehto-Wolf E, Zimmet P, Alberti KGMM, Keen H. Primary prevention of diabetes mellitus. In: KGMM Alberti, RA DeFronzo, H Keen, P Zimmet (eds), International Textbook of Diabetes Mellitus. Chichester, Wiley, 1992: pp. 1655 ±1673. 12. Haffner SM, Stern MP, Hazuda HP, Mitchell BD, Patterson JK. Cardiovascular risk factors in confirmed prediabetic individuals. Does the clock for coronary heart disease start ticking before the onset of clinical diabetes? J Am Med Assoc (1990); 263: 2893±2898. 13. Zimmet PZ, Alberti KGMM. The changing face of macrovascular disease in non-insulin-dependent diabetes mellitus in different cultures: an epidemic in progress. Lancet (1997); 350: S1± S4. 14. Traven ND, Kuller LH, Ives DG, Rutan GH, Perper JA. Coronary heart disease mortality and sudden death: trends and patterns in 35 ± 44-year-old white males, 1970± 1990. Am J Epidemiol (1995); 142: 45 ± 52. 15. Long S, O'Brien K, MacDonald K et al. Weight loss in severely obese subjects prevents the progression of impaired glucose tolerance to Type II diabetes. A longitudinal intervention study. Diabetes Care (1994); 17: 372± 375. 16. Eriksson K-F, LindgaÈrde, F. Prevention of Type 2 (non-insulin-dependent) diabetes mellitus by diet and physical activity. Diabetologia (1991); 34: 891±898. 17. Ramaiya K, Swai A, Alberti K, McLarty D. Lifestyle changes decrease rates of glucose intolerance and cardiovascular (CVD) risk factors: a sixyear intervention study in a high-risk Hindu Indian subcommunity. Diabetologia (1992); 35(suppl): A60. 18. Eriksson J, Lindstrom J, Valle T et al. Prevention of Type II diabetes in subjects with impaired glucose tolerance: The Diabetes Prevention Study (DPS) in Finland. Diabetologia (1999); 42: 793± 801.

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EVIDENCE FOR PREVENTION: TYPE 2 DIABETES

19. Keen H, Jarrett R, McCartney P. The ten-year follow-up of the Bedford survey (1962 ± 1972): Glucose tolerance and diabetes. Diabetologia (1982); 22: 73 ± 78. 20. Jarrett R, Keen H, Fuller J, McCartney M. Worsening to diabetes in men with impaired glucose tolerance (`borderline diabetes'). Diabetologia (1979); 16: 25 ± 30. 21. Stowers JM, Sutherland HW, Kerridge DF. Longrange implications for the mother. The Aberdeen experience. Diabetes (1985); 34(suppl 2): 106± 110. 22. Sartor G, SchersteÂn B, CarlstroÈm S, Melander A, NordeÂn AÊ, Persson G. Ten-year follow-up of subjects with impaired glucose tolerance. Prevention of diabetes by tolbutamide and diet regulation. Diabetes (1980); 29: 41 ± 49. 23. The Diabetes Prevention Program Research Group. The Diabetes Prevention Program. Design and methods for a clinical trial in the prevention of Type 2 diabetes. Diabetes Care (1999); 22: 623± 634. 24. Saltiel AR, Olefsky JM. Thiazolidinediones in the treatment of insulin resistance and type II diabetes. Diabetes (1996); 45: 1661± 1669. 25. Antonucci T, Whitcomb R, McLain R, Lockwood D. Impaired glucose tolerance is normalised by treatment with the thiazolidinedione troglitazone. Diabetes Care (1997); 20(2): 188± 193. 26. Chiasson J-L. The effect of acarbose on insulin sensitivity in subjects with impaired glucose tolerance. Diabetic Med (1996); 13(suppl 2): S23 ± S24. 27. Harris SB, Gittelsohn J, Hanley A et al. The prevalence of NIDDM and associated risk factors in native Canadians. Diabetes Care (1997); 20: 185±187. 28. Simmons D. Diabetes and its complications in New Zealand: an epidemiological perspective. NZ Med J (1991); 109: 245±247. 29. O'Dea K. Westernisation, insulin resistance and diabetes in Australian Aborigines. Med J Aust (1991); 155: 258±264.

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30. World Health Organization. Technical Report Series no. 844. Prevention of diabetes mellitus. Geneva, WHO, 1994. 31. Lee AJ, Bailey AP, Yarmirr D, O'Dea K, Mathews JD. Survival tucker: improved diet and health indicators in an Aboriginal community. Aust J Public Health (1994); 18(3): 277± 285. 32. Lee AJ, Hobson V, Katarski L. Review of the nutrition policy of the Arnhem Land Progress Association. Aust NZ J Public Health (1996); 20(5): 538± 544. 33. Lee A, Bonson A, Yarmirr D, O'Dea K, Mathews J. Sustainability of a successful health and nutrition program in a remote Aboriginal community. Med J Aust (1995); 162: 632± 635. 34. O'Dea K. Marked improvement in carbohydrate and lipid metabolism in diabetic Australian Aborigines after temporary reversion to traditional lifestyle. Diabetes (1984); 33: 596± 603. 35. Shintani T, Hughes CK, Beckham S, O'Connor HK. Obesity and cardiovascular risk intervention through the ad libitum feeding of traditional Hawaiian diet. Am J Clin Nutri (1991); 53: 1647S 1651S. 36. O'Dea K, Spargo R. Metabolic adaptation to a low carbohydrate-high protein (`Traditional') diet in Australian Aborigines. Diabetologia (1982); 23: 494± 498. 37. Heath GW, Leonard BE, Wilson RH, Kendrick JS, Powell KE. Community-based exercise intervention: Zuni Diabetes Project. Diabetes Care (1987); 10(5): 579± 583. 38. Gittelsohn J, Harris SB, Burris KL et al. Use of ethnographic methods for applied research on diabetes among the Ojibway-Cree in Northern Ontario. Health Educ Quart (1996); 23: 365±382. 39. Zimmet P. Lefebvre P. The global NIDDM epidemic. Treating the disease and ignoring the symptom. Editorial. Diabetologia (1996); 39: 1247±1248.

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5

Methodology for Physical Activity Assessment Edward W. Gregg and Andrea M. Kriska University of Pittsburgh, USA

Recent epidemiological studies have related physical activity to improved glucose tolerance, insulin sensitivity and reduced prevalence and incidence of diabetes in various ethnic groups (see (1, 2) for a review of the literature). Physical activity is also thought to protect against a wide range of other diseases and adverse outcomes, including cardiovascular disease, stroke, colon cancer, functional decline, and all-cause mortality (3 ± 11). Additionally, physical inactivity has been related to overall obesity (12), body fat distribution (13, 14), blood pressure (15 ±17), lipid abnormalities (18), and hemostatic factors (19), which are also concerns for people with diabetes. With this evidence of the multifactorial significance of physical activity for health, the measurement of physical activity has become an increasingly important part of epidemiologic methodology. Many researchers are interested in measuring physical inactivity as a potential risk factor in the development of diseases. Public health interventionists rely on physical activity assessment to identify subgroups at greatest risk for disease and=or to monitor the progress of intervention efforts. Both researchers and interventionists would like to be able to identify the specific levels of physical activity likely to provide the most protection. This issue was the focus of a National Institutes of Health workshop, in which a panel of experts in physical activity epidemiology emphasized the need to clarify and specify `the type, pattern, intensity, frequency, and total amount of physical activity required to enhance functional status and provide mental and physical health benefits' and `the need to identify variations in dose± response relationships between different populations' as they relate to disease prevention

(20). For all of these issues, physical activity measurement is a crucial link and sometimes a lingering hurdle. WHAT IS PHYSICAL ACTIVITY? Physical activity has been defined by Caspersen et al. as `any bodily movement produced by skeletal muscles that results in energy expenditure' (21). Components of total energy expenditure in a relatively sedentary individual include basal metabolic rate, which typically encompasses 50±70% of total energy, and the thermic effect of food, which accounts for another 7±10% (22). The remaining 20±43% is composed of energy expended through some type of `physical activity', which can then be subdivided into energy expended in general activities of daily living (such as bathing, feeding, and grooming) and occupation, transportation, sporting and other leisure activities (see Figure 5.1). Since the relative contribution of each of these components can vary considerably both within and among individuals and populations, valid and appropriate measurement of physical activity becomes a challenging task This measurement is further complicated by the fact that there are several health-related dimensions of physical activity (23). For example, in addition to quantifying physical activity based on the amount of energy expended, it can be quantified based on the manner in which energy is expended. In other words, physical activity can be measured according to its effects on different systems of the body by assessing attributes such as aerobic intensity, muscular resistance, degree of weight-bearing, and range of motion involved (see Figure 5.2). These

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

Figure 5.1 Schematic representation of components of total energy expenditure

Physical activity Muscular resistance/ Strength Range of motion/ Flexibility

Aerobic intensity/ Cardiovascular fitness Weight-bearing/ Bone loading

Figure 5.2 Health-related dimensions of physical activity

qualitative differences in physical activity may have implications for the prevention of specific diseases. For example, 100 calories burned swimming may be particularly beneficial to cardiovascular health and the prevention of related diseases but 100 calories expended weight-training may have a more favorable effect on bone mass or osteoporosis risk. Similarly, 100 calories expended in range of motion or flexibility training may be important for maintenance of physical function or avoidance of disability in older populations.

HOW HAS PHYSICAL ACTIVITY BEEN MEASURED? Physical activity assessment tools have been used to measure many dimensions and attributes of physical activity, with most focusing on the amount of energy expended. These tools can be categorized

into objective and subjective approaches and include but are not limited to: Objective approaches: Direct measures of energy expenditure such as the doubly-labeled water technique, or the respiratory chamber. * Movement counters which initially measured frequency of movement and have progressively modified to detect differences in speed and direction of movement. * Physiological measures of physical activity including heart rate monitoring and fitness testing. * Behavioral observation systems. *

Subjective measures: An array of questionnaires and surveys which require the individual to recall their past activity behavior. These tools vary in their ability to quantify the type, frequency, duration, and intensity of various occupational, transportation, leisure, and household activities over a variety of time-frames. * Activity logs and diaries for recording of specific activities. *

Each approach has both advantages and disadvantages that can vary based upon the population being studied and the research objectives. In epidemiology, subjective measures have typically been used for the practical assessment of physical activity in populations, and objective measures

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METHODOLOGY FOR PHYSICAL ACTIVITY ASSESSMENT

have been utilized to validate the subjective activity measures. Description of Some of the Popular Objective Approaches Doubly-labeled Water The doubly-labeled water (DLW) technique is the best estimate of free-living, total energy expenditure and is regarded as a gold standard of energy expenditure in validation studies. It involves the ingestion of isotopically labeled water, from which the amount of labeled hydrogen and oxygen eliminated can be measured over a set period of time, usually one to two weeks. The difference in hydrogen and oxygen turnover rates allows an estimate of carbon dioxide production and thus an estimate of total energy expenditure (24, 22). By subtracting the resting energy expenditure, which is determined using standard laboratory procedures of indirect or direct calorimetry, an estimate of energy expended during physical activity is obtained. This estimate is typically expressed in kilocalories expended per day or per week. Doubly-labeled water allows the measurement of physical activity in free-living populations, with no influence on behavior and no constraints on the time or setting of physical activity. The fact that it is non-reactive, or does not cause an alteration of the participants' behavior, is an important advantage over some other measures of physical activity such as diaries and movement monitors. However, the high cost makes it impractical for many epidemiologic studies. DLW is also only relevant for studying total energy expended in physical activities and provides no information about the intensity or the specific type of activity performed during the sample time frame. Furthermore, it is not applicable for the evaluation of physical activity for more than a 1 ± 2 week period of time unless repeat assessments are made. Activity Monitors These have been used to assess physical activity and, more often, to help validate subjective

53

measures of activity. Examples of these tools include pedometers, activity movement monitors and accelerometers. Pedometers, worn in the shoe or on the hip, estimate movement by responding to vertical movement of the body. Recent enhancements include the ability to adjust for stride length and body weight, for a prediction of kilocalories expended (25). The Large Scale Integrated (LSI) activity monitor (GMM Electronics, Verona PA) contains a cylinder with a ball of mercury that detects movement each time the cylinder is displaced at least 3 degrees from the horizontal plane. Accelerometers such as the Caltrac and more recently developed Tritrac accelerometers detect not only the frequency of movement, but also the acceleration and deceleration of movement in a single (Caltrac) and double plane (Tritrac). The size of these instruments ranges from slightly larger than a wristwatch to that of a cigarette package and they are typically worn on the hip. Physical activity assessed by pedometers and the LSI are typically expressed in counts or steps per day or week. For research purposes, activity assessed by accelerometers can be evaluated in terms of absolute frequency, or weighted by acceleration for an estimate of energy expenditure. Activity monitors have been used in both children and adults and have been shown to be significantly correlated with physical activity estimates obtained from questionnaires (26 ± 28). Activity monitors cannot detect the difference between level ground and inclines or increases in muscular resistance, however, reducing accuracy for the measurement of varied types of physical activity (29). Since activity monitors were originally designed to quantify walking and running activity, they become less accurate in the measurement of activities that are not similar to walking or running. They can also by affected by passive movement such as car or bus rides on bumpy roads. Heart Rate Monitoring This has been used in a variety of clinical and research settings. Since heart rate is directly related to oxygen consumption, it can be used to differentiate activity levels among individuals. Unfortunately, the relationship between heart rate

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

and oxygen consumption is considerably weaker at the low levels of physical activity which are typical of much of the population. Heart rate is also vulnerable to many non-exercise-related stimuli such as psychological stress, dietary intake, and temperature (30). The potential variability caused by these factors makes it less useful for comparison of relatively inactive individuals. Like doublylabeled water, heart rate monitoring is usually limited to a short time-frame. In addition, there is the potential for heart rate monitoring to be cumbersome, causing an alteration in activity behavior. Physical Fitness Cardiorespiratory fitness defined by measurement of oxygen consumption at a submaximal or maximal workload on a graded treadmill or bicycle ergometry, has been used extensively to validate physical activity assessment tools (31 ± 33). The rationale for using fitness as a validation method for physical activity extends from the consistent findings that aerobic activity improves cardiorespiratory fitness (34). When well-defined laboratory criteria are used, physical fitness testing can be highly reproducible and avoids some of the subjective pitfalls of questionnaires. Fitness provides an objective way of comparing individuals in the population and of evaluating progress in exercise interventions. Like physical activity, physical fitness has been shown to be protective against cardiovascular disease and all-cause mortality (35 ± 37) and may be one mechanism whereby physical activity prevents disease (38). However, physical fitness has a strong genetic component (39). Because of the potential confounding by genetics and the fact that physical activity and physical fitness are often not strongly related, physical fitness testing probably has a more important role as an independent measure of disease risk than as an estimate of physical activity (30). Subjective Measures Physical activity questionnaires and diaries have emerged as the tool of choice for physical activity assessment primarily because of practical consid-

erations but also because they can estimate subcomponents of physical activity such as frequency, duration, and intensity. Physical activity surveys do not directly alter the individual's behavior, but are subject to recall biases. These tools vary considerably in their complexity, from self-administered single questions to comprehensive interviewer-administered surveys of lifetime physical activity. Single question questionnaires have been used which ask an individual whether or not they are more active than others of their age and sex or whether they exercise long enough to break into a sweat. More complex questionnaires attempt to survey a wide range of popular activities over a selected time-frame. Time-frame Physical activity questionnaires are further distinguished by the time-frame that they cover. Diaries and logs may require the participant to record activities over 1 day, 3 days or the past week. Past-week recall surveys may query the frequency and duration of participation of activities performed over the past week. Examples of past-week recall surveys include the Harvard Alumni Questionnaire, and the 7-day Physical Activity Recall Questionnaire (5, 40, 41). Surveys with a past-week time-frame are less vulnerable to recall bias and are more practical to validate with objective tools than are questionnaires of a longer time-frame. However, since physical activity may vary with season, or as a result of an acute illness or time commitment assessment over a short time period is less likely to reflect `usual' behavior. Questionnaires of a longer time-frame, such as 1 year, may be more likely to represent usual activity patterns and have been used extensively in epidemiologic studies. Past-year physical activity has been assessed by questionnaires such as the Minnesota Leisure-time Physical Activity Questionnaire (MLTPQ), the Modifiable Physical Activity Questionnaire (modified version of the Pima activity questionnaire) and the Harvard alumni questionnaire. All of these questionnaires result in a summary estimate of physical activity expended per week averaged over the past year (42, 43, 5). The potential for recall bias is greater when measuring long-term activity patterns although these assess-

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ments are less likely to be influenced by acute changes in activity levels than questionnaires with a shorter time-frame. To account for these issues, some questionnaires include assessment over both a short and a long time period in order to obtain the best overall estimate of an individual's typical activity levels (43). Lifetime Physical Activity Few studies have attempted to assess a lifetime of physical activity. Since chronic diseases such as osteoporosis and cancer tend to have a long developmental period, it is potentially the longterm chronic exposure to physical inactivity that increases risk for disease. In addition, historical physical activity assessment has the advantage of being feasible to use in case-control studies of rare diseases and avoiding the expense and time of longitudinal studies. Early measures of historical physical activity categorized people according to employment history (44 ±46). This approach makes assumptions about the activity level associated with specific job titles and ignores any contribution of leisure physical activity. Other lifetime assessments have grouped subjects according to participation in high school or inter-collegiate athletics (47 ± 50). This approach ignores the contribution of physical activity outside of organized sports. Historical physical activity has also been assessed using physical activity surveys in a more comprehensive manner, evaluating the extent to which leisure-time physical activities were performed during specific age periods (26, 44, 51). Studies utilizing these historical physical activity questionnaires have demonstrated that people who participated in less leisure-time physical activity over their lifetime had lower bone mass, more hip fractures and are more likely to develop noninsulin-dependent diabetes mellitus (26, 52 ± 54). Historical physical activity assessment is obviously limited by problems with recall and the difficulty in validation. While an ideal study design would examine lifetime physical activity prospectively, the use of historical physical activity questionnaires as described above has enabled examination of factors that would otherwise require many years of study, thousands of participants, and high expense to conduct.

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Types of Physical Activity Assessed Physical activity questionnaires vary according to whether they assess leisure, transportation, occupational or household activities. Early studies in physical activity epidemiology estimated physical activity performed at work. (3, 4). Using a classification scheme such as the US Dictionary of Occupational Titles, created and published by the Department of Labor (55), individuals were categorized into groups of `sedentary, light, medium, heavy, or heavy work' (46). Job classification has the advantage of being relatively objective and less vulnerable to recall bias. However, misclassification of individuals is possible due to assumptions about the amount of activity expended in a given occupation and the fact that activity levels of a given occupation can vary across regions. More recent occupational physical activity questionnaires have been developed which query the frequency, duration and intensity of activities within a job rather than simply inquiring about the job title itself (43, 56). Most contemporary physical activity surveys only assess leisure-time activities that require an energy expenditure above that of daily living. Due to the decline in physical activity levels within occupations in most industrialized countries, it is assumed that assessment of leisure-time physical activity may provide the best representation of population-wise variation in physical activity. Similarly, it is assumed that the activities of daily living such as bathing or feeding are similar among most individuals within the population and that differences in these activities are less likely to contribute substantially to energy expenditure in a population. Leisure time and sporting activities are more distinctive behaviors with more specific starting and ending time, making recall by the participant more precise and definition and quantification easier for the researcher. Because of this focus on leisure activities, it is imperative that the activities queried are both comprehensive and representative of the population and culture being studied. Although this focus on leisure and sporting physical activity may be valid for younger and healthier populations, some have suggested that differences in activities of daily living or other lowlevel leisure activities may be the most important determinant of energy expenditure and physical activity in an older or sick population (57).

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

Therefore, questionnaires have been developed to assess physical activity at the low end of the physical activity spectrum. The Physical Activity Scale for the Elderly (PASE), the Yale Physical Activity Survey (YPAS), and the Modified Baecke questionnaire all query these lower-level leisure activities (27, 28, 58). Because these questionnaires have been developed recently, there has been little evaluation of the relationship between physical activity and disease in older populations. This remains an important area of future research (59). An Example of a Comprehensive Physical Activity Survey The most popular survey approaches measure the type, frequency (e.g. number of times per given time-frame), duration (e.g. number of minutes per session), and estimate the intensity (e.g. degree of vigor or metabolic cost) of physical activities performed during a particular time period. It is this comprehensive assessment of physical activity that has allowed for a more sensitive discrimination between individuals of different activity levels and lends itself to subanalyses based on type, duration, and intensity of activity. This approach has served as the basis for much of the epidemiologic research relating physical activity to the prevention of cardiovascular and other diseases. As described in Figure 5.3, the data obtained from a more extensive questionnaire format give

Duration

Frequency 3 times/week or month

X

2 hours each time

X

Intensity weighting 5 METS or (5 kcal/kg . h)

X

Bodyweight 70 kg

6 hours per week

30 MET-hours per week or 30 kcal/kg per week

2100 kcal/week

Figure 5.3 Steps in the computation of the summary estimates for the physical activity questionnaire

the researcher options to analyze data at several different levels. Multiplying the number of times per week (or month) of participation by the number of hours (or minutes) per time leads to an estimate of total duration of physical activity within a specific time frame. Comparisons can be made at this step in this process by comparing individuals based on the total time (frequency and duration) spent participating in physical activities (Figure 5.3). If possible, time spent in each activity can then be multiplied by an estimate of the relative intensity of that activity. These estimates are obtained from the literature and correspond to how vigorous the specific activity is thought to be. All of the activities are then summed and expressed in `MET-hours per week' (or kcal=kg per week), which can be converted to kilocalories per week if one knows the body weight of the individuals. However, this involves making an assumption about the weight of the individual throughout the time-frame which is being assessed. As an example of this computation procedure, the Modifiable Activity Questionnaire assigns a specific MET level to each activity based on average levels of energy expenditure determined from the literature (43). Rather than weighting each specific activity by its relative intensity, some surveys weight groups of relatively similar activities. For example, the Harvard Alumni Questionnaire assigns 5.0 kilocalories per minute to a group of activities deemed to be of low cardiovascular intensity (e.g. gardening, bowling, etc.), 7.5 kilocalories per minute to moderate intensity activities (e.g. dancing) and 10 kilocalories per minute for high-intensity activities (e.g. running) (5). Regardless of the method of intensity-weighting used, several assumptions are made by the researcher when incorporating intensity into the analysis process: Lists of MET values for most activities are available and provide the basis for calculations in physical activity questionnaires. When obtaining a MET or kcal value from a list, it is assumed to be representative of the manner in which the activity was performed by the individual. Since skill level varies for sporting activities and a wide range of paces may be selected for activities like cycling, walking, jogging, there may be considerable variation in the actual energy expenditure across subjects. Weighting physical activities by intensity also assumes that body-

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METHODOLOGY FOR PHYSICAL ACTIVITY ASSESSMENT

weight is proportional to resting metabolic rate and that the relative increase in metabolic cost of a specific activity above resting is constant from person to person regardless of bodyweight. Because of these assumptions made with physical activity questionnaires, estimates of physical activity obtained from them give a relatively limited assessment of absolute energy expenditure. However, since physical activity represents the most variable component of total energy expenditure, they are valuable in relative terms and can be used to rank individuals or groups of subjects within a population from the least to the most active. This relative distribution of individuals based on their reported levels of physical activity can then be examined according to its relationship to physiological parameters and disease outcomes. THE APPLICATION OF PHYSICAL ACTIVITY ASSESSMENT: POPULATION AND OUTCOME CONSIDERATIONS Considering the wide variety of approaches for assessing physical activity, the researcher is frequently left in a quandary when designing a specific study. Since physical activity can be defined in several ways, no single standard exists with which to measure physical activity. Time considerations often require the researcher to choose a survey that is a brief but efficient measure of the most common physical activities of a population. Given these imitations, characteristics of the population being studied and the outcome of interest emerge as important factors in the choice of a physical activity assessment tool. If the objective of the research is to evaluate the relationship between exposure to physical inactivity or activity and a particular disease outcome, it is important that the assessment tool: (1) accurately represents physical activity of the study population and (2) focuses on the component of energy expenditure that encompasses the greatest proportion of total energy expenditure. For these reasons, it is important to consider the culture, gender, and age ranges of the population. In industrialized, developed countries, recent surveys have focused on leisure activity because of the general decline in physical activity in most occupations. The focus on leisure-time physical activity in these countries differentiates active from

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inactive people more effectively than would an occupational physical activity questionnaire. However, occupational activity probably remains of greater importance in developing counties where much of the population have physically demanding occupations. Therefore, some questionnaires have included both leisure-time and occupational physical activity assessment. Physical activity patterns have traditionally differed between men and women Perhaps due to the historic tendency to conduct epidemiological research on men rather than woman, physical activity questionnaires have been more orientated around the types of leisure-time and occupational activities typically performed by men. Women tend to engage in less intense activity and in child care and household activities, all of which are difficult to assess. The use of commonly used questionnaires in women may be less sensitive to differences in activity levels within populations of women. If this occurs, true relationships between physical activity and disease could be obscured. Work is currently being done to more accurately assess physical activity in women. Since different dimensions of physical activity, such as frequency, duration, intensity, and type, could conceivably have different influences on risk factors for disease and disease outcomes, the choice of a physical activity assessment tool may be determined in part by the disease endpoint being studied. While the majority of health benefits seem to be linked principally to the total amount of activity performed, physical activity assessment could focus on other dimensions, including aerobic intensity, resistance to the muscular system, and weight-bearing, any of which could affect specific diseases or health outcomes. Whether aerobic intensity can influence risk for disease outcomes independent of energy expenditure is controversial and difficult to determine in population-based studies because subjects who participate in vigorous activities tend to expend more energy in general. Stronger relationships between physical activity and diseases or risk factors for disease have often been observed when physical activity is weighted by intensity. While this could be due to a true relationship between intensity of activity and prevention of disease, it may also be because higher intensity activities are easier to recall and may be more reliably measured

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(43). While some studies have shown favorable relationships between more vigorous activity and blood lipids, cardiovascular disease, and mortality (7, 37, 60), others have found that obesity, cardiovascular disease, Type 2 diabetes, and lipid abnormalities may be affected by even lowintensity exercise and thus seem to be largely determined by total energy expenditure (61 ± 63). In other words, these outcomes may be more related to the total time spent doing activities than the intensity of participation in specific activities. Type 2 Diabetes Mellitus The evaluation of the relationship between physical activity and Type 2 diabetes risk is complicated by the fact that Type 2 diabetes is a multifaceted disease with a variety of risk factors and disease endpoints, including obesity, insulin resistance and sensitivity, insulin production, glucose intolerance, diabetes incidence, and glycemic control. Evidence exists from experimental, intervention, and=or population-based studies that physical activity may have beneficial effects in most of these steps. (64 ±69, 52, 63, 70± 72). Based upon the findings of many studies, physical activity is most likely to influence glucose tolerance under conditions where insulin resistance is the major cause of the abnormal glucose tolerance (66, 65, 73, 67 ±69, 74). Several experimental studies have attempted to determine what characteristics of physical activity are most responsible for positive effects on insulin sensitivity. Some have suggested that physical activity may act through a cumulative effect of frequent exercise (64, 68, 75). This contention is based on the observation that a single exercise episode results in a temporary lowering of blood glucose and an enhancement of insulin sensitivity shortly after the exercise bout. If true, the frequency with which exercise is performed may be important. Research by Holloszy et al. (74) suggests that higher intensity physical activity is more likely to bring about the desired metabolic changes than are lower intensity activities. In contrast, other experimental work by Braun et al. (76) found equivalent effects of lower and higher intensity exercise on insulin sensitivity in females with NIDDM when the exercise was matched for total energy expenditure.

National physical activity recommendations and summary statements suggest that the majority of overall health benefits from physical activity are gained by performing moderate intensity activities. Whether or not this holds for Type 2 diabetes specifically is currently not known. However, in examining some of the prospective data, the largest and most consistent difference in risk of Type 2 diabetes occurs between those individuals who report no activity and those who report some activity. For example, when examining the association between frequency of reported vigorous activity per week in both nurses and physicians, most of the difference in incidence of Type 2 diabetes occurred between those who reported less than once per week of activity compared with those who were active a minimum of once per week (63, 70). In contrast, the protective effect of vigorous physical activity was found to be greater than that of moderate activity in male alumni of the University of Pennsylvania (72), although this may be due to the fact that the recall of higher intensity activities is more reliable (42). Epidemiologic studies suggest that individuals who are obese and historically sedentary are at the highest risk for Type 2 diabetes and are thus the best targets for physical activity interventions. Since intense exercise is not likely to be feasible or popular in these individuals, interventions will probably be more successful if they focus on lower intensity activities. Unfortunately, lower intensity physical activities are the most difficult to measure. Fortunately, the assessment of physical activity has made considerable progress in the last half century, enhancing the understanding of the relationship between exposure to different levels of physical activity and risk for a wide range of diseases. Because of the contribution of physical activity assessment tools, physical activity is now regarded as one of the fundamental behaviors necessary for a healthy lifestyle. It is likely that any improvement in the accuracy of these tools will only enhance the ability to observe true relationships between physical activity, health, and disease, serving as an incentive and challenge for future researchers. REFERENCES 1. Kriska AM, Bennett PH. An epidemiological perspective of the relationship between physical activity

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16. Reaven PD, Barrett-Conner E, Edelstein S. Relation between leisure-time physical activity and blood pressure in older women. Circ (1991); 83: 559± 565. 17. American College of Sports Medicine. 1994 Hypertension position stand. 18. Durstine JL, Haskell WL. Effects of exercise training on plasma lipids and lipoproteins. Exerc Sport Sci Rev (1994); 22: 477 ±522. 19. Bourey RE, Santoro SA. Interactions of exercise, coagulation, platelets, and fibrinolysis Ð a brief review. Med Sci Sport Exerc (1988); 20(5): 439± 446. 20. Blair SN, Wood PD, Sallis JF, Fox SM, King AC, Kriska A, Macera C, Marcus BH, Pratt M, Thompson PD. Workshop E: Physical activity and health. Preventive Med (1994); 23; 558± 559. 21. Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise and physical fitness: definitions and distinctions for health-related research. Public Health Rep (1985); 100: 126± 131. 22. Ravussin E, Rising R. Daily energy expenditure in humans: measurment in a respiratory chamber and by doubly labeled water. In: JM Kinney, HN Tucker, (eds) Energy Metabolism: Tissue Determinants and Corollaries Corollaries. New York, Raven Press, 1992: pp. 81 ±96. 23. Caspersen CJ. Physical activity epidemiology: concepts, methods, and applications to exercise science. Exerc Sport Sci Rev (1989); 17: 423± 473. 24. Schoeller DA, van Santen E. Measurement of energy expenditure in humans by doubly labeled water method. J Appl Physiol: Respirat Environ Exerc Physiol (1982); 53: 955± 959. 25. Montoye HJ, Kemper HCG, Saris WHM, Washburn RA. Measuring physical activity and energy expenditure. Champaign, IL, Human Kinetics, 1996. 26. Kriska AM, Sandler RB, Cauley JA, LaPorte RE, Hom DL, Pambianco G. The assessment of physical activity and its relationship to adult bone paramters. Am J Epidemiol (1988); 127: 1053 ±1063. 27. Washburn RA, Smith KW, Jette AM, Janney CA. The physical activity scale for the elderly (PASE): Development and evaluation. J Clin Epidemiol (1993); 46: 153 ±162. 28. Voorrips LE, Ravelli AC, Dongelmans PCA, Deurenberg P, Van Staveren WA. A physical activity questionnaire for the elderly. Med Sci Sport Exerc (1991); 23: 974± 979. 29. Haskell WL, Yee MC, Evans A, Irby PJ. Simultaneous measurement of heart rate and body motion to quantitate physical activity. Med Sci Sport Exerc (1993); 25: 109 ±115. 30. Laporte RE, Montoye HJ, Caspersen CJ. Assessment of physical activity in epidemiologic research: problems and prospects. Pub Health Rep (1985); 100: 131 146. 31. Jacobs DR, Ainsworth BE, Hartman TJ, Leon AS. A simultaneous evaluation of ten commonly used

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45. Vena JE, Graham S, Zielezny M, Swanson MK, Barnes RE, Nolan J. Lifetime occupational exercise and colon cancer. Am J Epidemiol (1985); 122: 357±365. 46. Vena JE, Graham S, Zielezny M, Brasure J, Swanson MK. Occupational exercise and risk of cancer. Am J Clin Nutr (1987); 45: 318± 327. 47. Sarna S, Sahi T, Koskenvuo M, Kaprio J. Increased life expectancy of world class male athletes. Med Sci Sport Exerc (1993); 25: 237± 244. 48. Fogelholm M, Kaprio J, Sarna S. Healthy lifestyles of former Finnish world class athletes. Med Sci Sport Exerc (1994); 26: 224± 229. 49. Sunman ML, Hoerr SL, Prague H, Olson HW, Quinn TJ. Lifestyle variables as predictors of survival in former college men. Nutr Res (1991); 11: 141± 148. 50. Frisch RE, Wyshak G, Albright TE, Albright NL, Schiff I. Lower prevalence of diabetes in female former college athletes compared with nonathletes. Diabetes (1986); 35: 1101± 1105. 51. Halioua L, Anderson JJB. Lifetime calcium intake and physical activity habits: independent and combined effects on the radial bone of healthy premenopausal women. Am J Clin Nutr (1989); 49: 534±541. 52. Kriska A, LaPorte R, Pettitt D, Charles M, Nelson R, Kuller L, Bennett P, Knowler W. The association of physical activity with obesity, fat distribution and glucose intolerance in Pima Indians. Diabetologa (1993); 36: 863± 869. 53. Jaglal SB, Kreiger SB, Darlington G. Past and recent physical activity and risk of hip fracture. Am J Epidemiol (1993); 138: 107± 118. 54. Greendale GA, Barrett-Connor E, Edelstein S, Ingles S, Haile R. Lifetime leisure exercise and osteoporosis. Am J Epidemiol (1995); 141: 951± 959. 55. US Department of Labor: US Dictionary of Occupational Titles. Washington, DC, Government Printing Office, (1939, 1949, 1965, 1977). 56. Montoye HJ. Estimation of habitual physical activity by questionnaire and interview. Am J Clin Nutr (1971); 24: 1113± 1118. 57. LaPorte RE, Adams LL, Savage DD, Brenes G, Dearwater SR, Cook T. The spectrum of physical activity, cardiovascular disease and health: an epidemiologic perspective. Am J Epidemiol (1984); 120: 507± 517. 58. Dipietro L, Caspersen CJ, Ostfeld AM, Nadel ER. A survey for assessing physical activity among older adults. Med Sci Sport Exerc (1993); 25: 628±642. 59. Caspersen CJ, Kriska AM, Dearwater SR. Physical activity epidemiology as applied to elderly populations. BaillieÁre's Clin Rheumatol (1994); 8: 7 ± 27. 60. Marrugat J, Elosua R, Covas M, Molina L, RubiesPrat J. Amount and intensity of physical activity, physical fitness, and serum lipids in men. Am J Epidemiol (1996); 143: 562± 569.

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61. Duncan JJ, Gordon NF, Scott CB. Women walking for health and fitness. How much is enough? J Am Med Assoc (1991); 266: 3295 ±3299. 62. King AC, Haskell WL, Young DR, Oka RK, Stefanick ML. Long-term effects of varying intensities and formats of physical activity on participation rates, fitness, and lipoproteins in men and women aged 50 to 65 years. Circ (1995); 91: 2596± 2604. 63. Manson JE, Rimm EB, Stampfer MJ, Colditz GA, Willett WC, Krolewski AS, Rosner B, Hennekens CH, Speizer FE. Physical activity and incidence of non-insulin-dependent diabetes mellitus in women. Lancet (1991); 338: 774± 778. 64. Koivisto VA, Yuki-Jarvinen H, DeFronzo RA. Physical training and insulin sensitivity. Diabetes= Metab Rev (1986); 1: 445± 481. 65. Lindgarde F, Saltin B. Daily physical activity, work capacity and gluscose tolerance in lean and obese normoglycemic middle-aged men. Diabetologia (1981); 20: 134 138. 66. Saltin B, Lindgarde F, Houston M, Horlin R, Nygaard E, Gad P. Physical training and glucose tolerance in middle-aged men with chemical diabetes. Diabetes (1979); 28: 30 ± 32. 67. Trovati M, Carta Q, Cavalot F, Vitali S, Banaudi C, Lucchina PG, Fiocchi F, Emanuelli G, Lenti G. Influence of physical training on blood glucose control, glucose tolerance, insulin secretion, and insulin action in non-insulin-dependent diabetes patients. Diabetes Care (1984); 7: 416± 420. 68. Schneider SH, Amorosa LF, Khachadurian AK, Ruderman NB. Studies on the mechanism of improved glucose control during regular exercise in Type 2 diabetes. Diabetologia (1984); 26: 355 ± 360.

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69. Ronnemaa T, Mattila K, Lehtonen A, Kallio V. A controlled randomized study on the effect of longterm physical exercise on the metabolic control in Type 2 diabetic patients. Acta Med Scand (1986); 220: 219± 224. 70. Manson JE, Nathan DM, Krolewski AS, Stampfer MJ, Willett WC, and Hennekens CH. A prospective study of exercise and incidence of diabetes among US male physicians. J Am Med Assoc (1992); 268: 63 ±67. 71. Dowse GK, Garaboo H, Zimmet PZ, Alberti GMM, Tuomilehto J, Fareed D, Brissonnette LG, Finch CF. High prevalence of NIDDM and impaired glucose tolerance in Indian, Creole, and Chinese Mauritians. Diabetes (1990); 39: 390± 396. 72. Helmrich SP, Ragland DR, Leung RW, Paffenbarger RS. Physical activity and reduced occurrence of non-insulin-dependent diabetes mellitus. N Engl J Med (1991); 325: 147± 152. 73. Krotkiewski M. Physical training in the prophylaxis and treatment of obesity, hypertension and diabetes. Scan J Rehabil Med (1983); suppl: 55 ± 70. 74. Holloszy JO, Schultz J, Kusnierkiewicz J, Hagberg JM, Ehsani AA. Effects of exercise on glucose tolerance and insulin resistance. Acta Med Scand (Suppl) (1986); 711: 55 ± 65. 75. Diabetes Care. Exercise and NIDDM: Technical Review. (1990); 13: 785± 789. 76. Braun B, Zimmerman MB, Kretchmer N. Effects of exercise intensity on insulin sensitivity in women with non-insulin-dependent diabetes mellitus. J Appl Physiol (1995); 78: 300± 306. 99. O'Dea K. Marked improvement in carbohydrate and lipid metabolism in diabetic Australian Aborigines after temporary reversion to traditional lifestyle. Diabetes (1984); 33: 596± 603.

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Part II

Caucasian Populations

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6

Ascertainment, Prevalence, Incidence and Temporal Trends Rhys Williams

Nuffield Institute for Health, Leeds, UK

INTRODUCTION Prevalence and incidence are fundamental concepts in epidemiology. In the study of the epidemiology of diabetes it is usually incidence which features more prominently in the discussion of Type 1 diabetes and prevalence in that for Type 2 diabetes. It is not difficult to see why this difference has arisen. The onset of Type 1 diabetes is frequently dramatic. It was once believed that these dramatic events signalled the start of the disease process. Now we know differentlyÐthat a prodromal period of variable duration is present in many, if not all, cases. For Type 2 diabetes, on the other hand, onset is usually insidiousÐthe disease may remain undetected for a considerable time. The concept of incidence is less satisfactory than for Type 1 diabetes and the frequency with which new cases are diagnosed or discovered is taken to be a more relevant measure of Type 2 diabetes occurrence. Both the frequency of diagnosis of new cases and prevalence are important items of information about the public health burden of Type 2 diabetes. The first is an indication of the requirement to investigate, diagnose and begin the process of education and behavioural change. The second is an indication of the extent to which health services and society as a whole are required to support individuals with the disease and is an indication of the likely future burden of diabetes complications. ASCERTAINMENT The ascertainment of people with Type 2 diabetes (or, more correctly, those with the Type 2 diabetes

process or processes going on) provides the epidemiologist with more areas of difficulty than the ascertainment of Type 1 diabetes. There are a number of interrelated reasons for this. None are insuperable but the means to overcome them are not always available. They are: *

*

As mentioned above, the fact that the clinical manifestation of Type 2 diabetes is much less dramatic than that of Type 1 diabetes. There must be many people who live and die without it being recognized that they have Type 2 diabetes. The identification of people with Type 1 diabetes is greatly facilitated by the fact that they are virtually all treated with insulin. Restriction of the ascertainment to those who were diagnosed at a young age and who have been treated with insulin virtually from the start of their disease effectively means that no other form of diabetes will be identified. The vast majority of those who are receiving oral hypoglycaemic medication have Type 2 diabetes. However, this statement is becoming less valid as other individuals (e.g. those designated as being in the IGT category) are being treated with these drugs. A substantial number of those with Type 2 diabetes will not be identified by the drugs they are prescribed nor by the equipment with which they are supplied. These are the `diet only' individuals, many of whom do not monitor themselves by means of blood or urine glucose testing. Unfortunately for the epidemiologist therapeutic practices differ in different populations so it is not just a simple matter of multiplying the number of oral hypoglycaemic-treated cases by

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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*

*

*

THE EPIDEMIOLOGY OF DIABETES MELLITUS

a constant to yield the total number of those with Type 2 diabetes. The identification of undiagnosed Type 2 diabetes is frequently by means of the oral glucose tolerance test (OGTT). The repeatability of the OGTT, even over short timespans, is not impressive. Partly because of the above, population-based registers for Type 2 diabetes are much less extensively developed than those for Type 1 diabetes. Recovery from Type 2 diabetes, though uncommon, is possible (with extensive weight loss, for example) and so cases once identified do not always remain as cases for the remainder of their lives.

The `capture-recapture' method for estimating the size of populations was developed in zoology, ecology and other subject areas as a means of

estimating the size of free-living populations when it was not possible to identify all individuals within those populations. Its extension to epidemiology has been strongly advocated (1) and it has been used in a number of topic areas, for example the prevalence of mental illness among the homeless (2). It has also been used for estimating the number of publications not identified in systematic reviews of the literature (3). In the last mentioned study, the alternative name of COMMA (comparison of multiple methods of ascertainment) has been advocated. The term `capture-recapture' is not ideal in the context of epidemiology since noone is being captured or recaptured in the sense with which these terms were used in ecology or zoology. COMMA has not been used in the field of Type 2 diabetes epidemiology to anything like the extent of its use in Type 1 diabetes (see elsewhere in this volume) although the advantages of doing so have

Table 6.1 Methods of case ascertainment and their advantages and disadvantages in relation to Type 2 diabetes Method of ascertainment

Advantages

Disadvantages

(A) Population-based survey (including biochemical testing)

1. Ascertainment of previously undiagnosed cases 2. Diagnostic criteria can be standardized 3. Accurate determination of denominator possible 1. Continuous monitoring of prevalence possible 2. Determination of denominator possible 3. Usually less costly than (A)

1. Usually costly 2. Usually a `one-off'Ð not a method for continuous monitoring

(B) Population-based survey (excluding biochemical testing) (C) Population-based register (D) Hospital records

(E) Primary care records

(F) Patients' organization(s) (G) Media advertisements

1. Continuous monitoring possible 2. Standardization of diagnostic criteria possible 1. Data are usually already being collected for other purposes

1. Data may be being collected for other purposes 2. Determination of denominator may be possible 3. Most people with diabetes will contact primary care 1. Database will exist for other purposes 2. Members are usually enthusiastic to participate 1. Can be a successful way to create interest

1. Ascertainment of previously undiagnosed not possible 2. Standardization of diagnostic criteria usually not possible (e.g. with self-report of diabetes) 1. Initial stages can be costly 1. Some individuals will never be identified 2. Standardization of diagnostic criteria possible but rarely accomplished in practice 3. Not usually possible to identify population denominator 1. Standardization of diagnostic criteria possible but rarely accomplished in practice

1. Coverage variable 2. Membership is likely to be biased by age, severity, socio-economic group 1. Response unpredictable 2. Response likely to be biased

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67

been pointed out (4). This is unfortunate since, as has been pointed out by Hook and Regall (5), this method should not be viewed as merely a desirable addition to a study attempting complete ascertainment. It should be an integral part of such studies since it is rarely possible to ascertain 100% of known cases and it behoves all researchers to estimate the number of cases they might have missed. Even though COMMA estimates are vulnerable to bias as a result of association between the individual methods of ascertainment (analogous to less than perfect population mixture between the capture and subsequent recapture in ecological studies), techniques exist for the estimation of these associations (6). When the methods of ascertainment are positively associated (as will frequently be the case in epidemiological studies), the estimated figure for the total population will be an underestimate. At the interface between epidemiology and public health policy we are familiar with working with estimates of disease occurrence that are likely to be underestimates. Provided we are aware of this then techniques such as COMMA, to provide `ascertainment corrected prevalence rates' (4), are of considerable use. Table 6.1 lists a number of methods of case ascertainment and their advantages and disadvantages in relation to Type 2 diabetes.

of the bars which represent diagnosed and undiagnosed diabetes and IGT. However, their overall message is clear Ð that, for each of these three categories, white US citizens have the lowest prevalence of all the groups identified. In Europe, two of the most recent population surveys in the summary by Pozza et al. (8) are those of Tuomilheto et al. (9) (Finland) and Forrest et al. (10) (United Kingdom). The former found prevalences of between 23% and 32% in people aged 65± 84. This survey was based on the modified OGTT and, therefore, detected those with undiagnosed as well as diagnosed diabetes. The proportion of undiagnosed cases was different in the sample from eastern Finland (56%) from that found in western Finland (72%). Forrest et al. (10) used a similar methodology but studied people over the age of 40. Their estimate for the total prevalence of diagnosed and undiagnosed diabetes in this age group was 4.6% of whom 56% were previously undiagnosed. These and other studies quoted by Pozza et al. and by other reviewers suggest that the proportion of previously undiagnosed cases detected in epidemiological studies of Caucasian populations in Europe ranges from just under 50% to around 75%.

PREVALENCE

As suggested at the beginning of this chapter, incidence is not a particularly useful concept in Type 2 diabetes. The time at which the disease process commenced is unknown for most, perhaps all, cases of this disease. The incidence at which new cases are diagnosed is a more valid concept and this has been explored in a number of studies, though very many fewer than have studied prevalence. In the United States the incidence with which new cases are identified has been studied both in cohort studies such as the Framingham study (11) and in successive cross-sectional studies such as the National Health Interview Survey (12). The latter, it has been noted (7), estimates that around a further 625 000 people with diabetes (both Type 1 diabetes and Type 2 diabetes) are diagnosed each year in the United States. Rates of diagnosis rise with age and, for the United States between 1990 and 1992, are higher in women (2.84 per 1000 population per year) than in men (1.97 per 1000 population per year) (7).

Kenny, Aubert and Geiss (7) have summarized the current situation with regard to the prevalence of Type 2 diabetes in the United States. They draw attention to the fact that prevalence estimates have increased steadily over the last 40 years. They estimate the current, overall prevalence of diabetes (Type 1 diabetes and Type 2 diabetes) to be 3.1% (in 1993). This represents around 7.8 million people. Over all ages, both sexes and all ethnic groups they report that around 90% of this 3.1% have Type 2 diabetes. Their Figure 4.7 contrasts prevalence estimates for non-Hispanic whites, non-Hispanic blacks, Mexican Americans and Puerto Rican and Cuban Americans. Unfortunately, this figure and their Table 4.3 which gives the data on which Figure 4.7 is based, is somewhat difficult to interpret in terms of the time periods stated (1976 ± 80 and 1982 ±84) and the shading in Figure 4.7 of the separate parts

INCIDENCE

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

One of the most useful advances in diabetes epidemiology over the last 30 years has been the standardization of biochemical criteria for the diagnosis of diabetes. West, e.g. (l3), first raised the issue of differences in the criteria that health professionals used to diagnosed diabetes. Since then, population-based surveys, if they use these standardized criteria, are comparable, provided the conditions for testing and presentation of data are similar. However, studies which are based on self-reported illness are still liable to temporal and cultural differences in the extent to which these are used in clinical practice and the stringency with which individuals are investigated before the disease label is applied. Cross-cultural studies of the use of these criteria by the health professions are required before valid comparisons can be made of self-reported newly diagnosed Type 2 diabetes. TEMPORAL TRENDS No Caucasian populations have been described in which rates of diagnosis of diabetes or Type 2 diabetes have fallen over time. Past trends are of some interest, however, it is in the prediction of future trends where descriptive epidemiology makes its most important contribution to health care policy Ð from the level of the individual practice to the national scale. Ruwaard (14) has provided detailed descriptions of the calculations required to predict the number of people likely to have diabetes in the population of the Netherlands in the year 2005. For any country, it is a simple matter to take existing estimates of incidence or prevalence and to multiply them by population numbers derived from census projections and thus to predict the numbers of new cases, or the number of people likely to be affected by the disease in future if the risk of acquiring the disease does not change. Thus, for the United Kingdom, taking prevalence estimates, for people aged 50 and over, from Neil et al. (15), population data for 1991 from the national census and official population projections for 2001 and 2021, estimates of the number of people in this age group likely to have diabetes can easily be made (see Table 6.2). Working on the assumption that there are approximately as many people with undiagnosed diabetes as with diagnosed diabetes, the projections can be enlarged to

Table 6.2 Estimates and projection of the number of adults, aged 50 and over and living in the United Kingdom, who are likely to have diabetes, diagnosed and undiagnosed in the years 1991, 2001 and 2021

Population Number (diagnosed diabetes) Number undiagnosed diabetes)

1991

2001

2021

17 972 000 501 000

19 442 000 553,000

23 380 000 640,000

501 000

553 000

640 000

* Using a prevalence figure of 2.8% for each (see text).

include totals for those likely to have undiagnosed diabetes. This simple method of forecasting (Ruwaard terms it the `static' model) is limited to the influence that demographic changes will have on prevalence. Any consideration of its implications has to be prefaced by the statement that it assumes no change in the genetic or environmental influences on diabetes prevalence and no change in life expectancy. Also, of course, its predictions are for all diabetes and not Type 2 diabetes. One of the only things of which we can be fairly confident is that there will be change in the individual risk of acquiring Type 2 diabetes over the next few decades. Population data on obesity, for example, gathered from industrial countries over the last three decades, frequently show an increase in the proportion of those considered obese. One example of this is the situation in the adult population of England. England's health strategy document Ð The Health of the Nation (16) Ð included, among its 28 health targets, a reduction, by the year 2005, of the proportion of the adult population regarded as obese by at least 25% for men and at least 33% for women. The first follow up report (17) showed that the proportion of those considered obese (BMI over 30 for both sexes Ðthe same definition as that used in setting the targets) was increasing, from 7 to 13% in men, and from 12 to 15% in women, between 1986=87 and 1991. Ruwaard's `dynamic' model (14) uses the `prevalence pool' concept to illustrate the factors influencing the Type 2 diabetes prevalence. Entry into the pool is by incidence (or, more strictly, diagnosis in the case of Type 2 diabetes). Exit is by death or might be by remission (for a few). The latter is considered so small that it is felt it can be

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ASCERTAINMENT, PREVALENCE, INCIDENCE AND TEMPORAL TRENDS

neglected. For the former, future predictions of the influence of diabetes on life expectancy are required. In the Dutch population the life expectancy of people with Type 2 diabetes, based on past experience, is thought not likely to change a great deal over the time-scale considered. The likelihood of an improved life expectancy for Type 1 diabetes patients will have a limited impact on future predictions of total diabetes since Type 1 diabetes, as in most other Caucasian populations, constitutes at most 20% of the population. Despite this, and because of the ageing of the population over the time period considered (up to the year 2005), the proportion of Type 2 diabetes patients in the population is likely to increase. The variables which need to be considered in the dynamic model of diabetes prediction (see Table 6.3) may be divided into those which are disadvantageous for the individual and the population, those which are neutral, and those which are advantageous. The first category contains any influence which leads to an increase in the prevalence of risk markers. The rising prevalence of obesity and decrease in physical activity can be included in this category. In the neutral category are the rise in the proportion of the elderly in the population (already included in the static model) and any change in ethnic mix. The advantageous category includes any improved health outcome leading to greater life expectancy and, as variables of unknown but possible influence, improved Table 6.3 Variables to consider in the `dynamic' model for predicting future prevalence of Type 2 diabetes * Category `Disadvantageous' Increase in prevalence of risk markers `Neutral' Demographic changes `Advantageous' Improved health outcome Reduction in prevalence of risk markers Remission to normal glucose tolerance

Variable Rising prevalence of obesity Decrease in physical activity Increasing proportion of elderly * * Change in ethnic mix Increased life expectancy Improvement in conditions of early growth and development Net loss in number of diseased individuals

* Modified from Ruwaard (14), by permission. * * Also taken into account by `static' model.

Table 6.4 Estimates of the number of people with diagnosed diabetes in the Netherlands in 2005 according to `static' and `dynamic' models Model

1980 (observed)

2005 (predicted)

Increase * * (%)

Dynamic 2{

191 000

268 000 (1.65% of popn.) 339 000 (2.1% of popn.) 355 000 (2.2% of popn.)

41

Dynamic 1{

191 000 (1.35%) 191,000

Static * *

78 86

* Adapted from Ruwaard (14), by permission. * * Prevalence in 2005 compared with that in 1980. { For explanation see text.

conditions of early growth and development and any remission from Type 2 diabetes to normal glucose tolerance. A comparison of the results of Ruwaards's static and dynamic models is given in Table 6.4. Two variants of the dynamic model are used. Version 1 uses a constant incidence over time (i.e. does not assume any change in risk markers and no remission) and takes into account only population changes and changes in life expectancy. The second makes the assumption that incidence rates will rise with time. Further details of the model and these assumptions are given in Hoogenveen et al. (19) and in an appendix to Chapter 5 of Ruwaard (14). Validation of such models is possible by historic validation (the prediction of past prevalence, using data from the more distant past and their comparison with directly observed data) and sensitivity analyses (the exploration of the effects of varying one or more parameters in the model). The dynamic model is more sensitive to variations in incidence predictions than it is to changes in prevalence estimates of equal magnitude. CONCLUSION Although Type 2 diabetes is less common in Caucasian populations than in many others and although, in global terms, the burden of Type 2 diabetes in Caucasian populations makes a modest contribution to the global impact of the disease, the predicted rise in prevalence of Type 2 diabetes in Caucasians makes it an important future public health problem. Despite longstanding knowledge of many of the risk markers for Type 2 diabetes,

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

population measures for the reduction of the prevalence of these markers and thus the prevalence of Type 2 diabetes have yet to make any substantial impact.

9.

REFERENCES

10.

1. LaPorte R. Assessing the human condition: capturerecapture techniques. Br Med J (1994); 308: 5 ± 6. 2. Fisher N, Turner SW, Pugh R, Taylor C. Estimating numbers of homeless and homeless mentally ill people in north east Westminster by using capturerecapture analysis. Br Med J (1994); 308: 27 ± 30. 3. Spoor P, Airey M, Bennett C, Greensill J, Williams R. Use of the capture-recapture technique to evaluate the completeness of systematic literature searches. Br Med J (1996); 313: 342± 343. 4. LaPorte RE, McCarty D, Bruno G, Tajima N, Baba S. Counting diabetes in the next millennium. Diabetes Care (1993); 16: 528±534. 5. Hook EB, Regal RR. The value of capture-recapture methods even for apparently exhaustive surveys. The need for adjustment for sources of ascertainment intersection in attempted complete ascertainment studies. Am J Epidemiol (1992); 135: 1060±1067. 6. Cormack RM. Log-linear models for capturerecapture. Biometrics (1989); 45: 395± 413. 7. Kenny SJ, Aubert RE, Geiss LS. Prevalence and Incidence of Non-Insulin Dependent Diabetes. In: MI Harris (ed), Diabetes in America, 2nd edn, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases. NIH Publication No. 95 ± 1468, 1995: pp. 45 ± 67. 8. Pozza G, Garancini P, Gallus G. Prevalence and incidence of NIDDM. In: R Williams, L Papoz, J

11. 12.

13. 14.

15. 16. 17. 18.

Fuller (eds), Diabetes in Europe. London=Paris, John Libbey=Les Editions Inserm, 1994: pp. 21 ± 38. Tuomilheto J, Nissinen A, Kivela SL, Pekkanen J, Kaarsalo E, Wolf E, Aro A, Punsar S, Karvonen MJ. Prevalence of diabetes mellitus in elderly men aged 65 ± 84 years in eastern and western Finland. Diabetologia (1986); 29: 611± 615. Forrest RD, Jackson CA, Yudkin JS. Glucose intolerance and hypertension in North London: the Islington diabetes survey. Diabetic Med (1986); 3: 338±342. Wilson PWF, Anderson KM, Kannel WB. Epidemiology of diabetes in the elderly. The Framingham Study. Am J Med (1986); 80 (suppl. 5A): 3 ± 8. Adams PF, Benson V. Current estimates from the National Health Interview Survey. National Center for Health Statistics. Vital Health Stat (1991); 10, no. 181. West KM. Laboratory diagnosis of diabetes: a reappraisal. Arch Intern Med (1966); 117: 187± 191. Ruwaard D. Diabetes mellitus: from epidemiology to health policy. PhD thesis. Cip-Gegevens Koninklijke Bibliotheek, den Haag. The Hague, 1996. ISBN 90-9009749-X. Neil HAW, Gatling W, Mather HM et al. The Oxford Community Diabetes Study. Diabetic Medicine (1987); 4: 539± 543. The Health of the Nation: a Strategy for Health in England. London, Her Majesty's Stationery Office (HMSO), 1992. The Health of the Nation One Year On. London, Her Majesty's Stationery Office (HMSO), 1993. Hoogenveen RT, Ruwaard D, Velde LJK van der, Verkleij H. Incidentie, prevalentie en ziekteduur. Een dynamische beschrijving. Report no. 958606002. Bilthoven, the Netherlands: National Institute of Public Health and Environmental Protection, 1989.

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7A

Type 1 Diabetes: Global Epidemiology Marjatta Karvonen, A. Sekikawa, R. LaPorte, J. Tuomilehto and Eva Tuomilehto-Wolf

National Public Health Institute, Helsinki, Finland

Type 1 diabetes mellitus is one of the major noncommunicable diseases in children aged 14 years or under (1). Although the etiology of Type 1 diabetes is still unknown, it is currently assumed that both genetic (2 ± 5) and environmental factors (6 ± 17) operate together in a process in the pancreatic beta-cells, leading to the onset of diabetes. The role of HLA genetics in the etiology of Type 1 diabetes is well understood, but neither the mode of inheritance nor how environmental factors may initiate=trigger the process which leads to the destruction of the beta-cells and to the onset of diabetes are clear. Recent advances in research into the etiology and natural history of Type 1 diabetes have increased knowledge about different types of diabetes to such an extent that the primary prevention of Type 1 diabetes is becoming a reality. The increasing incidence of Type 1 diabetes, the severity of its complications and the increasing socio-economic costs favor immediate preventive action. Unfortunately, the means for the primary prevention are not yet available. REGISTRATION OF TYPE 1 DIABETES WORLDWIDE During the 1970s published reports suggested wide geographical differences in incidence of Type 1 diabetes (18 ±24), but the lack of standardized data made it difficult to determine the true magnitude of the worldwide variation in Type 1 diabetes morbidity. Standardized collection and analysis of epidemiological data of Type 1 diabetes started in the 1980s and since the mid-1980s Type 1 diabetes registries have been established in many parts of the world. The Diabetes Epidemiology Research

International Group (DERI) (25) played the key role in collecting standardized Type 1 diabetes incidence data between the late 1970s and the mid1980s, rendering possible the direct comparison of data between countries. The DERI group reported the incidence from 15 countries between 1978 and 1989 (25, 26). The findings indicated a large global variation with the difference between the highest and lowest rates being about 60-fold. The highest incidence rate was found in Finland, followed by the other Nordic countries Sweden and Norway, and the lowest rate was seen in Japan and in Mexico. The authors proposed a strong correlation between the age-adjusted incidence rates and the average yearly temperature and also the existing north ±south gradient of incidence rates; possibly indicating the potential of environmental factors in the etiology of disease. By the end of the 1980s a considerable number of Type 1 diabetes registries had published incidence data worldwide. Most of the information on Type 1 diabetes incidence comes from the geographical regions with a high or intermediate level of incidence, e.g. Europe and North America, where a large number of Type 1 diabetes registries have been established since the mid-1980s. Although several standardized registries have recently been established, data on Type 1 diabetes incidence from Asia and Africa are still sparse. The published data facilitate the descriptive comparison of Type 1 diabetes incidence and the variation of the occurrence of disease roughly throughout most of the northern hemisphere. The collaborative research project EURODIAB was also established in the late 1980s (27) to gather information about Type 1 diabetes in Europe. The World Health Organization Project, the Multi-

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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Table 7A.1 WHO DIAMOND centers by continents Africa 8 centers: Algeria (Oran) Mauritius Nigeria (Lagos) Sudan (Gezira province) Tunisia (Beja, Gafsa, Kairouan, Monastir) Asia 30 centers: China (Beijing, ChangChun, Dalian, Harbin, Heilongjiang provicne, Henan province, Huhuhot, Jiangxi province, Jilin province, Oiaokou District, Shanghai, Shenyang, Tie Ling, Urumgi, Zi Gong, Zunyi City) Hong Kong India (New Delhi, New Delhi 2) Israel Japan (Chiba, Hokkaido, Okinawa) Korea (Seoul) Kuwait (Safat) Pakistan (Karachi) Philippines (Quezon City) Russia (Novosibirsk) Taiwan (Taipei) Oceania 3 centers: Australia (New South Wales) New Zealand (Auckland, Canterbury)

Europe 42 centers: Austria Belgium (Antwerpen region) Bulgaria (East-Bulgaria, West-Bulgaria) Croatia Denmark (3 counties) Estonia Finland France (4 regions) Greece (Attica Region) Hungary (18 counties) Italy (Catania province, Chieti and Pescara, Lazio region, Lombardia region, Pavia province, Region Marche, Sardinia, Torino, Turin province) Latvia Lithuania Luxembourg Netherlands (5 regions) Norway (8 counties) Poland (Cracow, Wielkopolska region Portugal (Algarve region, Coimbra, Portalegre) Romania (Bucharest region) Slovakia Slovenia Spain (Barcelona, Madrid) Sweden UK (Aberdeen, Leicestershire, Northern Ireland, Oxford region)

North America 9 centers: Canada (Alberta, Prince Edward Island) USA (Allegheny county, Chicago, Colorado, Jefferson county, Memphis, Philadelphia, Washington) Central America and West Indies 9 centers: Barbados Dominican Republic Cuba Mexico (Verarcuz) British Virgin Islands UK Virgin Islands USA South America 14 centers: Argentina (Avellaneda, Berazatequi, Cordoba, Corrientes, Rosario, Salta) Brazil (Sao Paulo state) Chile (Santiago) Colombia (Barranquilla, Santafe de Bogota DC) Paraguay Peru (Lima) Uruguay (Montevideo) Venezuela (Caracas 2)

Adapted from (28) by permission.

national Project for Childhood Diabetes (DIAMOND) was started in 1990 (28) (Table 7A.1 and Figure 7A.1). Other collaborative groups were also formed, such as the DIABALT group in the Baltic Sea region (29), the Iberian Heritage groups to study the epidemiology of Type 1 diabetes in the Iberian peninsula and in the Americas (30), the Italian Heritage group (31) and the Arab Heritage group (32). All of these have employed standardized protocols so that the incidence data around the world can now be compared. ASCERTAINMENT OF TYPE 1 DIABETES CASES The ascertainment of Type 1 diabetes cases has not always been complete in many epidemiological

studies for several reasons. Type 1 diabetes represents only 4± 6% of all cases of diabetes and thus we are dealing with small numbers of affected subjects. Type 1 diabetes may have been misdiagnosed or misclassified in some circumstances. Health policy and the lack of regional or national registries has been a serious disadvantage for accurate case ascertainment. Until the beginning of the 1980s different methods were applied to Type 1 diabetes ascertainment. Classical Type 1 diabetes rarely poses a diagnostic problem. However, most studies of the incidence and prevalence of diabetes in young people have not clearly discriminated between Type 1 diabetes and Type 2 diabetes cases. Almost all studies of Type 1 diabetes limit themselves to cases diagnosed before 30 years of age, although Type 1 diabetes can also occur after the age of 30

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73

Figure 7A.1 Age-specific incidence (per 100 000 population) of Type 1 diabetes in children aged 14 years or under. Data for boys and girls have been pooled. The populations are arranged in ascending order according to the incidence. Data from Diamond Centers 1990± 1994. Adapted from (28) by permission.

years. In ideal circumstances, the necessary conditions for epidemiological studies and a high level of case ascertainment are uniformity of population, lack of migration and an established high rate of cooperation of health care personnel. Effective public health care systems, the existence of computer systems for recording diagnoses, national registries, the involvement of a central person registry or any other means of tracing

individuals throughout the country are remarkable tools that will help the epidemiologist perform a good prevalence or incidence study. Type 1 diabetes is a relatively infrequent disorder that needs a large population sample for its study to avoid under- or overestimation of incidence or prevalence, consequently the true incidence or prevalence rates cannot be established by population-based studies. Different methods were applied

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to Type 1 diabetes ascertainment until the beginning of the 1980s. Traditional methods for monitoring Type 1 diabetes have been too expensive or too inaccurate for broad accurate national programs. It has been therefore rather difficult to compare results from different surveys carried out before the early 1980s. Capture-Recapture Method The methodological improvement to counting Type 1 diabetes is the capture-recapture approach published by LaPorte and coworkers (33) at the beginning of the 1990s. Although the capturerecapture method does not give an estimate of incidence and prevalence where the point estimate is the absolute truth, it comes closer to the truth than the fairly inadequate systems used earlier. When an attempt is made to identify new cases of Type 1 diabetes multiple sources are used. People with diabetes are identified from hospitals, pediatricians, schools, chemists and so on. To determine the numerator (e.g. the number of new cases), the researchers typically aggregate the sources, sorting out duplicates. That total is used for determining the incidence rates (34 ± 38). This incidence rate should be considered as a crude rate, because it assumes that the aggregate of the lists represents all of the cases in the population. It assumes that the number of missing cases is zero and undercounting is ignored. In the capture-recapture method attention is paid to the duplicates, because they provide important information about the degree to which cases may have been missed. These cases represent recaptured people who have diabetes and the degree of undercounting is estimated and the rates of Type 1 diabetes are corrected accordingly, thus yielding a corrected ascertainment rate. TYPE 1 DIABETES INCIDENCE WORLDWIDE Incidence rates reported over a period of 20 years, from the late 1970s to the early 1990s (Table 7A.2) (17, 25, 27, 39 ±84) show the vast geographical variation in Type 1 diabetes incidence. This large variation worldwide was also seen in small `pockets' of countries, e.g. around the Baltic Sea.

Between continents the variation in incidence showed that the lowest incidences were found in Asia, followed by Oceania (Australia and New Zealand), South and North America, and the highest rates were found in Europe. The greatest within-continent variation in incidence appeared in Europe, varying from the highest (36 per 100 000) in Finland (43) to the lowest (3 per 100 000) in Macedonia (45). Incidence in Northern Europe was in general higher than the rates observed elsewhere in Europe, excluding Sardinia where the incidence 34 per 100 000 was the second highest in the world after Finland (74). Between 1970 and 1976 the incidence in Denmark was about one-half of that in Sweden, but by the end of the 1980s Denmark (22=100 000) had reached the incidence rates of Sweden (24=100 000) and Norway (21=100 000) and had joined the high-risk Nordic countries (17, 27, 71). The lowest rate among the Nordic countries was in Iceland (9=100 000) (70), the northern-most country in Europe. In North America the range of the intracontinental variation in Type 1 diabetes incidence was also large ranging from 95

1987 ± 1990

95

1982 ± 1991

?

0.8

0.9

1975 ± 1986 1971 ± 1985

99 94

27.0 9.6

1980 ± 1986 1970 ± 1979 1965 ± 1989

? >90 >90

1965 ± 1979 1978 ± 1988

100 93

1979 ± 1985

96

1985 ± 1989

93

1978 ± 1981

Central America and the West Indies Barbados [54] 1982 ± 1991 Cuba [25] 1978 ± 1980 Mexico Mexico City [25] 1984 ± 1986 Puerto Rico (USA) [55] 1985 ± 1989 Virgin Islands (GB)[56] 1979 ± 1988 Black South America Argentina Avellaneda [57] Brazil State of SaÄo Paulo [58] Chile [59] Peru Lima [41]

Estimate of ascertainment (%)

M=F Excess * * *

T

8.1 7.8 2.4

7.0 2.1

505 0.81 0.75

0.24 0.39

165 32

0.8

0.89

0.13

86

20.8 10.0

23.9 9.8

1.30 0.96

0.30 0.04

92 919

21.6 20.2

16.2 16.2

18.9 18.2

1.33 1.25

0.33 0.25

204 166

15.1 8.5 15.8

13.2 12.1 18.4

14.2 10.3 17.1

1.14 0.70 0.86

0.14 0.42 0.17

1414 146 38

?

16.4 7.1 9.9 15.1 3.4 11.3 12.7 9.1 10.6 9.6

14.5 10.5 14.9 16.2 10.6 14.8 13.6 13.3 19.5 9.1

15.5 8.7 12.4 15.6 7.0 13.4 13.3 11.0 15.2 9.4

1.13 0.68 0.66 0.93 0.32 0.76 0.93 0.68 0.54 1.05

0.13 0.48 0.51 0.07 2.12 0.31 0.07 0.46 0.84 0.06

1048 117 128 134 41 215 109 86 17 48

94 ?

2.5

2.8

5.0 2.7

0.89

0.12

37 267

0.4

0.7

0.6 10.0

0.57

0.75

100

6.9

4.8

5.9

1.44

0.44

27 17

? ? 92

6.3 1.8

Number of cases

10.1

1985 ± 1990

6.7

1987 ± 1991 1990 ± 1991

>90 100

1985 ± 1991

85

1980 ± 1991 1986 ± 1990 1975 ± 1980

94 100

1974 ± 1986 1980 ± 1989 1980 ± 1989 1992 ± 1993

100 100 100 92

1985 ± 1988

?

1983 ± 1989

96

5.8 2.2

9.5 2.8

7.6 2.5

30 0.61 0.79

0.64 0.27

52 78

0.4

0.6 1.5 4.4

0.7 2.4 6.7

0.6 2.0 5.5

0.82 0.63 0.66

0.22 0.60 0.52

75 22 296

1.3

2.1

0.62

0.62

283

16.6

14.1

1.7 1.8 1.7 15.4

1.18

0.18

86

0.6

0.8

0.7

0.74

0.35

71

4.6

4.9

4.7

0.94

0.07

205

(continued )

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76

THE EPIDEMIOLOGY OF DIABETES MELLITUS

Table 7A.2 (continued) Region

Study period

Country and area Oceania Australia New South Wales [64] Western part [65] New Zealand [25] Auckland Canterbury Europe Austria [27] Belgium Antwerpen [27] Bulgaria Sofia [66] Croatia Zagreb [67] Denmark 3 counties [27] Estonia [68] Finland [43] France 4 regions [27] Greece [69] Hungary [27] Iceland [70] Italy Lazio [27] Liguria [73] Lombardia [27] Marche [72] Pavia [76] Piedmond [77] Sardinia [74] Eastern Sicily [27] Turin [75] Latvia [68] Lithuania [68] Luxemburg [27] Macedonia [45] Malta [78] Netherlands [27] Norway 8 counties [27] Poland 3 cities [27] 9 western prov. [27] Rzeszow [79] Portugal 3 regions [27] Romania Bucharest [27] Slovakia [80] Slovenia [27] Spain Catalonia [27] Madrid [81] Sweden [100] United Kingdom [82] Leicestershire [25] Northern Ireland [27] Oxford [27] Scotland [25] Tayside [25] Yorkshire [83] Yuogoslavia Belgrad [84]

Estimate of ascertainment (%)

Incidence

M=F Ratio

M

F

T

M=F Excess * * *

Number of cases

1991 1985 ± 1992

99 100

14.1 19.0

16.1 25.6

15.0 22.2

0.88 0.74

0.14 0.35

188 84

1978 ± 1985 1981 ± 1986

? 100

9.0 10.2

10.5 12.9

9.8 11.6

0.86 0.79

0.17 0.27

233 39

1989 ± 1990

94

7.9

7.5

7.7

1.05

0.05

205

1989 ± 1990

100

9.2

10.4

9.8

0.88

0.13

31

1987 ± 1991

?

1988 ± 1992

>90

7.7

6.7

7.2

1.15

0.15

72

1989 ± 1990 1983 ± 1988 1987 ± 1992

99 95 100

21.5 10.6 37.6

21.4 9.9 33.5

21.5 10.3 35.7

1.00 1.07 1.12

0.01 0.07 0.12

66 208 2062

1989 ± 1990 1992 1989 ± 1990 1970 ± 1989

100

7.8 6.7 7.7 9.9

7.8 6.5 7.5 8.8

7.8 6.6 7.6 9.4

1.00 1.04 1.03 1.13

0.00 0.04 0.03 0.13

261 137 256 120

1989 ± 1990 1987 ± 1991 1989 ± 1990 1990 ± 1992 1988 ± 1992 1989 ± 1990 1989 ± 1990 1989 ± 1990 1984 ± 1988 1983 ± 1988 1983 ± 1988 1977 ± 1986 1985 ± 1991 1980 ± 1987 1989 ± 1990

100 99 100 100 100 91 93 100 99 100 100 90 ± 100 97 ? 99

7.2 11.5 7.6 7.9 11.4

5.8 12.0 5.9 8.3 9.9

1.24 0.95 1.29 0.95 1.15

0.24 0.05 0.29 0.05 0.15

117 117 193 50 31

34.1 11.2 8.8 6.2 6.5 12.1 2.4 12.7 12.3

27.2 9.0 7.6 6.8 7.0 12.6 2.5 14.6 12.4

6.5 11.7 6.8 8.1 10.7 9.4 30.7 10.1 8.2 6.5 6.8 12.4 2.5 13.6 12.4

1.25 1.24 1.15 0.91 0.93 0.96 0.95 0.87 1.00

0.25 0.24 0.15 0.10 0.08 0.04 0.05 0.15 0.00

219 52 148 215 336 16 112 66 58

1989 ± 1990

100

22.3

19.3

20.8

1.16

0.16

158

1989 ± 1990 1989 ± 1990 1980 ± 1992

100 100 99

5.7 5.3 5.4

6.0 5.8 4.8

5.8 5.5 5.1

0.95 0.91 1.14

0.05 0.09 0.14

102 164 122

1989 ± 1990

91

10.1

4.9

7.5

2.06

1.06

25

1989 ± 1990 1992 1988 ± 1990

100 95 100

4.6 8.0 5.2

5.7 9.9 7.7

5.1 8.9 6.5

0.81 0.80 0.68

0.24 0.25 0.48

47 112 56

1989 ± 1990 1985 ± 1988 1978 ± 1987 1988 1965 ± 1981 1989 ± 1990 1989 ± 1990 1976 ± 1983 1980 ± 1983 1978 ± 1990

95 90 99 89 >90 95 98 100 100 98

10.5 11.3 25.0 13.8 8.7 17.8 17.8 20.0 19.7

10.6 10.5 23.8 13.3 8.6 15.4 14.9 19.4 22.1

10.6 10.9 24.4 13.5 8.7 16.6 16.4 19.7 20.0 13.6

0.99 1.08 1.05 1.04 1.01 1.16 1.19 1.03 0.89

0.01 0.08 0.05 0.04 0.01 0.16 0.20 0.03 0.12

297 501 3836 1600 272 130 161 1856 64 1490

1982 ± 1992

90

7.6

8.6

8.1

0.88

0.13

259

99 100

6.7

M, male; F, female; T, total *Age of 20 years or under. * *Age of 17 years or under. * * *Negative value indicates a female excess and positive value a male excess in the incidence of Type 1 diabetes. Reproduced from Karvonen et al. (1997) Diabetes=Metabolism Reviews. 13(4): 275 ± 91, by permission.

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TEMPORAL TRENDS IN TYPE 1 DIABETES INCIDENCE Most of the Type 1 diabetes registries have been using consistent case definitions and registration practices for a relatively short time, and only a few registries have been active for a longer period, e.g. 20 years or more. Therefore temporal trends in Type 1 diabetes incidence have been difficult to study in detail. In some countries temporal changes in Type 1 diabetes incidence have been reported (Table 7A.3). Several registries (17, 27, 43, 56, 62, 71, 86±93, 101±103) have reported a change in Type 1 diabetes incidence in North America, Europe and Oceania during various periods between 1966 and 1992. During these years an increase in Type 1 diabetes incidence has been observed in several European countries, whereas in the North American continent occasional peaks Table 7A.3 Reported increase in Type 1 diabetes incidence from 1960 to 1996 Temporal increase reported

Study period

Reference

1966±1986 1965±1984 1965±1984 1970±1989 1949±1984 1976±1985 1973±1982 1980±83, 1990±92 1970±1985 1958±1987 1985±1991 1977±1983

92 43 89 27 87 90 71 91 50 229 80 17

1966±1981 1966±1986

93 101

Asia Japan Hokkaido Israel Kuwait

1966±1986 1975±80, 1980±89 1983, 1992±93

102 103 62

Oceania New Zealand (White) Australia

1966±1986 1985±1992

88 65

North America United States Allegheny county Virgin Islands

1965±1989 1979±1988

50 56

Europe Austria Finland France Denmark Germany Hungary Norway Netherlands Poland Sardinia Slovakia Sweden United Kingdom Leicestershire Scotland

77

have been reported but no clear increasing trend has been documented. The greatest temporal increase was found in Europe, especially in the northern part of the continent. For instance, in Finland the increase in Type 1 diabetes incidence has been almost linear for 30 years. The regression-based change in incidence was about 2.8% per year from 1965 to 1992 (43). During the 1970s the increase was steepest in 5± 9 year olds and since the mid-1980s in those younger than 5 years old at diagnosis (43). In Sweden, an other Scandinavian country with a high Type 1 diabetes incidence, the increasing trend in incidence was seen during 1977 to 1983, mainly among children younger than 10 years of age. Since then the increase has been leveled off (17). The increase in incidence is not restricted to northern Europe, since increasing long-term trends were also reported for Sardinia (74) and Austria (92). The increasing trend in Type 1 diabetes cannot be explained by the change in ascertainment rates, because most of the data have been collected or confirmed, or both, according to the requirements established by the DERI group (25). Whether the increase in incidence can be explained by change in genetic susceptibility in the population, or by increasing penetrance of the susceptibility genes in the population, or by an increase in the pool of genetically susceptible individuals is not known. The incidence of Type 1 diabetes has been rising most rapidly in northern Europe where populations have been relatively stable and homogeneous in many countries and where perinatal and infant mortality has drastically decreased during the last few decades. It is very likely that some changes in environmental determinants of Type 1 diabetes have significantly contributed to the rising incidence, but their role has not as yet been determined. The epidemic-like temporal fluctuation in incidence was reported in several countries (29, 46, 50, 52, 56, 85, 94±99). Although the data are few it appears that in the 1980s in North America the years of the highest Type 1 diabetes incidence were between 1981 and 1984 (50, 52, 56, 95, 99) and in Asia and Oceania most of the peak years were found almost at the same time in 1983 and 1985 (94, 96). In Europe the peak years were between 1983 and 1988, particularly in 1985 and 1986 (29, 50, 75, 94, 100), and in Africa the peak year was 1988 (82). The

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

fluctuation of the high-incidence years from one continent to another may indicate the possibility of pandemics of an infectious disease functioning at least as a triggering factor for the onset of the Type 1 diabetes. Other local environmental exposures may also play a role. AGE AND SEX DISTRIBUTION OF TYPE 1 DIABETES INCIDENCE During childhood the incidence of Type 1 diabetes increases with age, and in both sexes the peak in incidence is seen in puberty. This peak seems to occur somewhat earlier in females than in males. Most of the individual studies have been based on a relatively small number of cases and therefore the overall picture with regard to a possible sexassociated effect has remained unclear. Nevertheless, only 20 ±50% of all patients with Type 1 diabetes are diagnosed during childhood, although most of the cases are diagnosed before the age of 30 years (104, 105). Age-specific Incidence of Type 1 Diabetes Age- and sex-specific incidence in children aged 14 years or under has been evaluated in 76 populations worldwide. Age-specific Type 1 diabetes incidence has been reported in 5-year age groups (0 ± 4, 5 ± 9 and 10 ± 14 years) in several countries, summarized in Tables 7A.2 and 7A.4. The variation in the level of incidence between populations became wider with increasing age. Among children aged 14 years or under the incidence of Type 1 diabetes varied from 0.4 to 35.7=100 000 worldwide. Among the youngest children, aged 4 years or under, the incidence varied from 0.3 to 27.6=100 000 and in children aged 5 ± 9 years the range of variation was from 0.4=100 00 to 38.9=100 000, being widest in children aged 10 ± 14 years from 0.6=100 00 to 40.4=100 000. Sex Ratio in Type 1 Diabetes Incidence The male=female excess in the incidence of Type 1 diabetes is shown in Table 7A.4 and Figures 7A.2 and 7A.3. Depending on the sex-

specific incidence the sex ratio was calculated in the following way: A. The incidence is higher in males than in females: (IM=IF) 1 B. The incidence is higher in females than in males: (IF=IM) 1 To distinguish the female excess from the male excess in incidence, the rate ratio is expressed as negative in case of female excess in incidence. The female excess in the incidence of Type 1 diabetes was found in 59% of these populations worldwide (Table 7A.5). The largest female excess in Type 1 diabetes incidence (ratio 2.12) was found in the Black population in Jefferson County, Alabama USA, while the largest male excess (ratio 1.06) in incidence was found in Portugal. However, the number of reported cases in these two populations representing the extremes was small (41 and 25 respectively) and thus the result should be interpreted with caution. The association between the sex ratio in incidence and the level of incidence of Type 1 diabetes is shown in Figure 7A.4. This association was not linear (the Spearman rank-order correlation between male-to-female ratio and the level of incidence of Type 1 diabetes among 76 population, was 0.37, p = 0.001) indicating that the number of the populations with a male excess in the incidence of Type 1 diabetes was higher at higher levels of incidence. The same phenomenon was seen when the level of incidence was divided into quartiles and the populations were grouped into three groups: 25% of the populations were regarded as having a low incidence, 50% an intermediate incidence, and 25% a high incidence. A female excess in incidence was found in 88% of the low-incidence populations (15.0=100 000) a clear male excess in incidence was found in 68% (Table 7A.5). Sex Ratio by Age Group The incidence data in 5-year age groups were available for both sexes for 62 populations (Table 7A.4 and 7A.5). Among children aged 4 years or under the male excess in incidence was

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TYPE 1 DIABETES: GLOBAL EPIDEMIOLOGY Table 7A.4 Age-specific incidence of Type 1 diabetes in children aged 14 years or under (per 100 000 population) Region

Incidence

Country and area

Males

Females

Male or female excess * * 0±4 y

5±9 y

15.6

0.15

0.48

0.12

25.1 11.9

38.5 11.4

2.37 0.33

0.25 0.05

0.20 0.16

9.1 8.2 8.7

18.5 17.0 15.4

20.8 23.3 30.6

0.28 0.21 0.55

0.29 0.01 0.26

0.35 0.42 0.44

22.5 12.0

7.3 3.4

17.6 11.7

22.4 18.3

0.38 0.44

0.07 1.66

0.00 0.53

21.5 2.9 7.0

20.0 1.4 14.6

6.2 8.3 3.6

24.3 14.9 11.3

26.0 8.3 12.6

0.61 4.93 0.94

0.13 4.14 0.61

0.30 4.93 0.16

Central America and the West Indies Cuba [25] 1.5

3.4

2.5

1.9

3.0

3.5

0.27

0.13

0.40

South America Brazil [58] State of SaÄo Paulo

2.4

5.3

10.1

7.6

12.7

8.5

2.17

1.40

0.19

0.3 2.2

0.3 4.5

0.6 6.5

0.3 2.7

1.5 9.1

0.6 8.2

0.08 0.23

4.80 1.02

0.05 0.26

0.5

0.8

2.2

1.5

2.6

4.2

2.00

2.25

0.91

0.4 14.9

0.2 16.7

1.0 18.7

0.4 10.7

0.5 14.8

0.9 17.9

0.00 0.40

1.50 0.13

0.11 0.05

2.3

2.8

8.7

1.9

4.9

7.8

0.21

0.75

0.12

7.1

14.0

21.0

8.5

17.6

22.1

0.20

0.26

0.05

4.2

5.2

20.9

10.8

7.2

20.1

1.57

0.39

0.04

5.1

8.2

10.3

4.5

8.3

9.9

0.13

0.01

0.04

3.7

12.5

11.6

5.8

11.2

14.1

0.57

0.12

0.22

2.2

9.1

12.3

3.4

4.8

12.6

0.55

0.90

0.02

16.5 3.1 28.0

12.4 11.0 39.6

35.6 18.1 45.3

8.5 3.2 27.1

26.2 13.5 38.1

29.6 13.3 35.5

0.94 0.03 0.03

1.11 0.23 0.04

0.20 0.36 0.28

Africa Libya Benghazi [47] North America Canada [25] Prince Edward Isl. Montreal United States North Dakota [25] Wisconsin (part) [25] Rochester [25] Colorado [51] * Non-Hispanics Hispanics Jefferson county [52] White Black San Diego [25]

Asia China Shanghai [40] Israel [27] Japan Hokkaido [25] Republic of Korea Seoul [39] Kuwait [62] Russia Novosibirsk [63] Oceania Australia New South Wales [64] New Zealand Canterbury [25] Europe Austria [27] Belgium Antwerp [27] Croatia Zagreb [67] Denmark 3 counties [27] Estonia [48] Finland [43]

0 ±4 y

5 ±9 y

10 ±14 y

0±5 y

5±9 y

10±14 y

2.0

5.8

13.9

2.3

8.6

28.6 7.7

31.3 11.6

32.2 14.1

8.5 5.8

11.6 9.9 13.5

23.9 17.1 12.2

29.0 33.1 21.3

10.1 4.9

18.8 4.4

10.0 1.4 7.0

10±14 y

(continued )

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Table 7A.4 (continued) Region

Incidence

Country and area

France 4 regions [27] Greece [69] Hungary [27] Italy Marche [72] Lazio [27] Liguria [73] Lombardia [27] Sardinia [74] Eastern Sicily [27] Pavia [76] Turin [75] Latvia [68] Lithuania [68] Macedonia [44] Malta [78] Netherlands [27] Norway [27] 8 counties Poland 3 cities [27] 9 western prov. [55] Rzeszow [79] Portugal 3 regions [27] Romania Bucharest [27] Slovakia [80] Slovenia [27] Spain Catalonia [27] Sweden [100] United Kingdom [82] Leicestershire [25] Northern Ireland [27] Oxford [27] Scotland [25] Tayside [25] Yorkshire [25] Yuogoslavia Belgrade [84]

Males

Females

Male or female excess * *

0± 4 y

5± 9 y

10 ±14 y

0 ±5 y

5 ±9 y

10 ±14 y

0±4 y

5±9 y

5.4 2.8 4.5

8.6 4.7 8.2

9.5 10.5 10.4

3.8 2.2 4.3

6.9 5.9 7.6

4.3 4.7 8.5 4.8 22.4 12.7 7.0 5.0 3.2 4.1 1.6 6.1 6.8

10.9 10.1 13.2 9.7 40.1 10.1 8.6 7.8 5.4 6.9 2.2 12.0 11.7

8.1 6.7 13.1 8.4 36.9 10.9 16.7 11.8 10.5 8.6 3.5 20.2 18.5

4.5 5.3 8.3 4.8 19.6 2.6 2.4 4.8 3.7 2.9 1.2 9.1 6.1

13.4

26.3

27.3

1.7 2.7 3.7

7.1 5.6 5.6

9.4

10±14 y

12.7 10.1 10.8

0.42 0.27 0.05

0.25 0.19 0.08

0.34 0.04 0.04

11.4 6.6 15.9 5.3 32.0 14.1 11.2 6.8 6.9 8.6 3.4 15.6 13.3

8.5 5.6 12.3 7.6 28.4 10.1 14.2 9.9 10.0 9.6 3.1 19.5 17.7

0.05 0.13 0.02 0.00 0.14 3.89 1.85 0.03 0.16 0.41 0.38 0.49 0.12

0.05 0.53 0.21 0.83 0.25 0.40 0.30 0.16 0.28 0.25 0.56 0.30 0.14

0.05 0.20 0.07 0.11 0.30 0.08 0.18 0.19 0.05 0.12 0.14 0.04 0.05

7.9

26.2

23.8

0.70

0.00

0.15

8.3 7.5 7.2

3.6 1.5 2.9

6.1 5.9 4.9

8.3 10.0 6.7

1.12 0.80 0.27

0.16 0.05 0.15

0.00 0.33 0.08

8.8

11.9

2.0

5.5

7.1

3.70

0.60

0.68

0.7 6.9 3.1

2.5 9.9 3.5

10.5 7.0 9.0

5.1 6.2 2.5

6.0 11.4 6.5

5.9 11.9 14.3

6.29 0.12 0.40

1.40 0.15 1.10

0.78 0.70 0.60

4.7 16.8 10.0 5.1 11.4 15.2 13.7 17.0 9.8

9.8 25.4 12.9 8.5 15.5 14.3 20.4 17.7 13.1

17.0 31.6 18.8 12.3 26.5 23.9 25.9 29.9 18.2

3.4 14.5 9.7 3.4 13.6 11.3 12.2 15.6 9.6

10.9 26.3 13.6 8.2 13.8 13.4 19.9 20.7 13.4

17.7 29.4 16.7 14.1 18.7 20.0 25.8 18.2 16.9

0.38 0.16 0.03 0.50 0.19 0.35 0.12 0.09 0.02

0.11 0.04 0.05 0.04 0.12 0.07 0.03 0.17 0.02

0.04 0.08 0.13 0.15 0.42 0.20 0.00 0.64 0.08

4.6

7.6

10.5

3.3

10.3

11.9

0.39

0.36

0.13

M, male; F, female; T, total *Age 17 years or under. * *Negative value indicates a female excess and positive value a male excess in the incidence of Type 1 diabetes. Reproduced from Karvonen et al. (1997) Diabetes=Metabolism Reviews. 13(4): 275 ± 91, by permission.

found in 66% of the populations, whereas in the age group of 5± 9 years there was a female excess in 63% of the populations. In the age group 10± 14 years there was again a male excess in incidence in the majority, 60% of the populations.

A female excess in Type 1 diabetes incidence among children aged 4 years or under was found in 64% of the low incidence (15.0=100 000) incidence a male excess in incidence was found. In children aged 5±9 years a female excess in incidence was found in the populations with a low and an intermediate incidence, whereas 71% of the populations with a high incidence had a male excess. In the oldest age group (10±14 years old) a male excess in incidence was also found in children aged 10±14 years, in 56% of the populations with an intermediate and in 77% of the populations with high incidence, whereas of the populations with a low incidence 55% had a female excess in incidence. The sex ratio in Type 1 diabetes incidence diverged between continents (Table 7A.6). Europe

was the only continent where the slight male excess in incidence was seen: 55% of the populations had a male excess in the incidence of Type 1 diabetes. The sex ratio within Europe varied from a female excess in Slovenia (ratio 0.48) to the male excess in Portugal (ratio 2.06). In the North American continent a female excess in incidence was found in 67% of the populations and the greatest female excess (ratio 2.12) was in the Black population of Jefferson County, Alabama, USA. The male excess in incidence was found mainly in the northern part of the continent, in Colorado, Wisconsin and North Dakota in the USA and on Prince Edward Island, Canada. The number of populations from other continents is relatively small. Among populations from Asia, Central

Reproduced from Karvonen et al. (1997) Diabetes=Metabolism Reviews. 13(4): 275±91, by permission.

AUS, Australia; BEL, Belgium; BRA, Brazil; BUL, Bulgaria; CAN, Canada; CHN, China; GBR, Great Britain, GRE, Greece; Croatia, HRV; ITA, Italy; JPN, Japan; KOR, Korea; LBY, Libya; MEX, Mexico; NZL, New Zealand; POL, Poland; POR, Portugal; RUS, Russia; ESP, Spain; TZA, Tanzania; USA, United States of America; w, White; n-w, non-White; b, Black; h, Hispanic; n-h, non-Hispanic

Figure 7A.3 A male to female excess in the incidence (per 100 000 population) of Type 1 diabetes in children aged 14 years or under for boys and girls in age groups: 0±4 years, 5± 9 years and 10±14 years. Negative value indicates a female excess and positive value a male excess in the incidence of Type 1 diabetes.

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TYPE 1 DIABETES: GLOBAL EPIDEMIOLOGY

America and the West Indies only two out of ten populations had a male excess in incidence, and among populations from South America, Africa and Oceania there was a female excess in incidence

Table 7A.6 The global distribution of the populations with a male or female excess in the incidence of Type 1 diabetes

Table 7A.5 Number of populations (%) with a low (85th percentile from NHES, increased from 15% to 21%

among youth aged 12±19 years (82). Crosssectional anthropometric surveys of Mexican American children were conducted in Brownsville, Texas in 1972 and again in 1983 (83). Mean BMI and triceps skinfold increased significantly over the 11-year interval except among boys >15 years old. In preparation for an intervention study in 4th grade Mexican American children in Texas, baseline data were collected in 1997±98 on 173 subjects: 21% of boys and 18% of girls were overweight, defined as age- and sex-specific BMI >85th percentile of the 2nd National Health Examination Survey (84). Of these children 60% reported a firstor second-degree relative with diabetes. Other investigators in the US report similar high levels of overweight along with physical inactivity in children and adolescents in various geographic locations. A survey of 522 schoolgirls aged 10±18 in 1991 recorded overweight (>85th percentile in NHANES-1) in 22% of non-Hispanic white, 37.6% of non-Hispanic black, and 26.7% of Hispanic girls in Lynn, Massachusetts (85). More than 75% of these girls reported watching television >2 hours per day, and only 13% participated in strenuous physical activity >3 times per week. In preparation for an intervention trial among 4th graders in Baltimore, survey data were collected in 1995 from 785 students from primarily African American, low-income schools. They reported, on average, watching >4 hours of television per day (86). A US national survey of adolescents conducted in 1996 demonstrated large ethnic differences in reported inactivity, but less of a disparity in moderate-vigorous physical activity (87). Average television=video use was 20.4 hours=week for nonHispanic blacks and 13.1 for non-Hispanic whites. Non-Hispanic black and Asian girls had the lowest levels of physical activity. The increasing prevalence of obesity and physical inactivity among children provides a disconcerting glimpse of future generations, as recent longitudinal studies of the rate of fat accretion in children have revealed that a major determinant is parental fatness (88). INSULIN RESISTANCE AND HYPERINSULINEMIA IN HEALTHY CHILDREN AND ADOLESCENTS A reasonably comprehensive literature is emerging on puberty and insulin metabolism. In a study of

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TYPE 1 DIABETES IN YOUNG PEOPLE WORLDWIDE

normal, European-origin children (14 prepubertal and 19 pubertal), Amiel et al. showed them to exhibit selective insulin resistance which may have served to enhance the anabolic effect of insulin in proteins (89). A much larger study of insulin resistance (357 healthy children, ages 10 ±14; 73 were African American) demonstrated a significant decrease in insulin sensitivity through puberty, resolving to near prepubertal levels by Tanner stage 5 (90). Girls were more insulin resistant than boys at every pubertal stage; approximately 50% of this difference could be accounted for by adiposity as reflected by skinfold thickness. Non-Hispanic white boys were more insulin resistant than African Americans, and there was no significant ethnic difference among girls, controlled for BMI, adiposity and blood pressure. The authors suggest that the initial reports of greater insulin resistance among black compared to white children could have resulted from the small numbers in these previous studies. However, black : white differences in insulin resistance and acute insulin response (AIR) were observed in another recent study of 95 prepubertal children that incorporated careful dietary measures (91). Black children had higher vegetable= fruit and lower dairy intake than whites, and they were more insulin resistant and had higher AIR. Dietary intake did not account for the ethnic differences in AIR, controlled for social class and adiposity. An elevated fasting insulin concentration is recognized as a risk factor for Type 2 diabetes. The longitudinal Pima Indian study provides information on the predictive value of obesity, family history, and insulinemia in childhood for subsequent development of Type 2 diabetes (92). Overweight was closely correlated with fasting insulin levels and family history of diabetes; in those with a positive family history, obesity was the best predictor of diabetes risk. Compared with non-Hispanic white children, Pima children had consistently higher fasting insulin levels despite similarities in age, height, and fasting glucose concentration (93). Multiple linear regression on (log) insulin demonstrated a significant interaction of race and relative weight; the models included age, age2, and fasting glucose, and accounted for 49% of the variance in boys and 52% of the variance in girls. Using oral glucose tolerance tests, the Bogalusa (Louisiana) group demonstrated significantly

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higher 0 ±60 minute insulin areas in non-diabetic black children compared with whites aged 5 ±17, controlled for age, weight, height and Tanner stage (94). Fasting and 2-hour insulin concentration in 1975 ±76 was higher among young Nauruans (ages 8 ±19) who developed impaired glucose tolerance or Type 2 diabetes over the next 11 years, than among those who remained normoglycemic (95). These studies corroborate other investigations of metabolic physiology that suggest that there are differences among racial groups in the anatomical and pathophysiological correlates of glucose intolerance (64). In particular, African ±American children with equivalent body fatness to white children have less abdominal fat, higher insulin concentrations, and lower insulin sensitivity (91,96). It is not clear whether these differences influence the clinical presentation of glucose intolerance in the different racial groups, but one would expect African-American children to be more vulnerable than white children to alterations in insulin secretion because of their dependence on elevated secretion to compensate for greater insulin resistance. Likewise, they would be more vulnerable than other groups to further decreases in insulin sensitivity, known to emerge during puberty. Whether this has implications for the etiology of Early 2 remains to be seen. POLYCYSTIC OVARY SYNDROME; ACANTHOSIS NIGRICANS Polycystic ovary syndrome is typically diagnosed in patients with symptoms of androgen excess, i.e. infertility, hirsuitism or oligomenorrhea, and elevated serum androgen levels, and is associated with insulin resistance. The prevalence of polycystic ovary syndrome is 20% of asymptomatic women in England as detected by ultrasound (97,98) Among 18 ± 40-year-old Asian Indian women in England the prevalence on ultrasound was >50%, and polycystic ovary syndrome was significantly associated with acanthosis nigricans and elevated fasting blood glucose, in addition to the recognized symptoms of hirsuitism, infertility and menstrual irregularities (99). Using a short intravenous glucose tolerance test, the presence of polycystic ovary syndrome in these women accounted for a reduction in insulin sensitivity comparable to that seen in women who had

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diagnosed Type 2 diabetes without polycystic ovary syndrome ( 60 mmol gluc=l-min for polycystic ovary syndrome without diabetes, vs. 68 mmol gluc=l-min for Type 2 diabetes without polycystic ovary syndrome). There was no relationship to blood pressure, insulin sensitivity, or lipids. INSIGHTS FROM THE CHICAGO CHILDHOOD DIABETES REGISTRY The population-based Chicago Childhood Diabetes Registry has been ascertaining diabetes with onset 27 Kg=M2 at onset, were selected; in addition, those who responded to specific questions during the interview were considered likely to be Type 2. These questions elicit information on cessation of insulin use after the `honeymoon' period, as well as on current treatment with oral agents. Those who do not meet one or more of the above criteria were considered to have type I diabetes. Using these criteria, we we were able to begin to address the question of secular increases in Early 2. Early 2 Incidence The proportion of currently registered cases classified as Early 2 as defined above is 28.3% among non-Hispanic blacks (n ˆ 704), and 21.9% among Hispanics (n ˆ 315). Recognizing the obvious biases related to incomplete ascertainment of those with more recent onset, we saw an increase in the number with Early 2 over time (Figure 7C.3). Yet even during the earliest years, the proportion who were likely to have Type 2 diabetes based on

Currently Available Data: Hospital Records : unusual, atypical, or poss. type 2? Obese, acanthosis , P C O S ? Interview (217): Using OHA? Stopped insulin w/o DKA after >6 m o duration?

Entire Registry All insulin-tx’d AA and L children 300 mg=24 h) was correlated with: (1) duration of diabetes; (2) decrease in glomerular filtration rates (GFR, measured with 125 I-iothalamate infusion) and increase in serum creatinine, and all these patients were hypertensive. Incipient nephropathy (AER 30±300 mg=24 h) was correlated with duration of diabetes and 80% of this group were hypertensive. Analysis in terms of the duration of diabetes and the presence or absence of hypertension showed that subjects who remained normotensive had normal renal function regardless of duration of diabetes (normal GFR and serum creatinine). In hypertensive subjects with Type 2 diabetes, Chaiken found: (1) a decrease in GFR with duration of diabetes of greater than 1 year; and (2) with Type 2 diabetes > 10 years, 36% had impaired renal function (GFR < 80 ml=m2 and=or serum creatinine > 1.4 mg=dl; 75% of these subjects have microalbuminuria or proteinuria. Within the group with longstanding diabetes, subjects who developed their hypertension after the diagnosis of diabetes were more likely to have nephropathy compared to those who developed hypertension prior to or at the time of diagnosis of diabetes (17=20 [85%] vs 7=13 [54%] respectively, p < 0.05), suggesting that nephropathy resulted in hypertension. Within the first year of diagnosis of Type 2 diabetes in African Americans, Chaiken (157) showed an absence of microalbuminuria in subjects (mean age 47 years) with or without hypertension. In contrast, Goldschmid (154) reported 30% to have microalbuminuria (mean age 52 years). The reason for this difference is unknown but may be due to the older age, or delay in presentation of diabetes in Goldschmid's patients. Similarly to Chaiken, he found the risk factors for nephropathy were duration of diabetes and hypertension; multivariate analysis showed HbAlC did not predict nephropathy. Dasmahaptra (156) reported 50 t of 116 African American clinic-based patients had increased AER, but did not report data for new-onset patients. AER correlated with age of onset, hypertension and BMI but not with duration of diabetes, age, HbA1C or lipids. Thus, early diabetic nephropathy, characterized by microalbuminuria, is associated with duration

of diabetes and hypertension. The role of glycemia is not clear. It is not known whether urinary microalbumin excretion rates are associated with cardiovascular risk as in some groups (158, 159). Early glomerular hyperfiltration has been variably associated with subsequent nephropathy in Type 1 (160±163) but not Type 2 diabetes (164±167). Chaiken reports hyperfiltration in 36% (15=42) of newly diagnosed (240 mg=dl, (B) LDL-cholesterol >160 mg=dl, (C) HDL-cholesterol 250 mg=dl Source: Reproduced from (69) by permission

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diabetic subjects with hypertension. The majority of non diabetic African Americans and whites do not have hypertension (60% and 70% respectively). The majority of diabetics do have hypertension: 63% ± 80% among African Americans and 40 ±60% among whites.

7. 8.

Mortality of Diabetes 22-year mortality data from the NHANES I study showed the age-adjusted mortality rate for nonHispanic blacks with Type 2 diabetes was 23% higher than for non-Hispanic whites (179). The mortality for African American women and men with diabetes was significantly higher than for those without diabetes (40% and 50% respectively) (179, 180). Diabetes was listed as an underlying cause of death in only 7.7% of diabetic men and 13.4% of diabetic women. This confirms that mortality data among African Americans (and others) with diabetes suffers from severe underreporting: the data are derived from hospital death certificates which list proximal cause but not underlying illness (1). Mortality rates for African American and whites are similar at age < 15 years for Type 1 diabetes (0.1 per 100 000 population) (181 ± 184). REFERENCES 1. National Center for Health Statistics: Current estimates from the National Health Interview Survey, 1994. Vital Health Statistics 10 (193), 1995. 2. Harris MI, Hadden WC, Knowler WC, Bennett PH. Prevalence of Diabetes and impaired glucose tolerance and plasma glucose levels in the US population age 20 ±74 yrs. Diabetes Care (1987); 36: 523± 534. 3. Hadden WC, Harris MI. Prevalence of diagnosed diabetes, undiagnosed diabetes and impaired glucose tolerance in adults 20 ± 74 years of age, United States, 1976± 1980. NCHS Vital and Health Statistics Series II, No. 237, 1987 4. Reed ET. Caucasian genes in American Negroes. Science (1969); 165: 762± 768. 5. Engerman SL, Genovese ED. Race and Slavery in the Western Hemisphere. Princeton, NJ, Princeton University Press, 1975: pp. 107± 128. 6. Kenny SJ, Aubert RE, Geiss LS. Prevalence and incidence of non-insulin dependent diabetes. In: MI Harris, CC Cowie, MP Stern, EJ Boyko, GE Reiber,

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108. Lipton RB, Fivecoate JA. High risk of IDDM in African American and Hispanic children in Chicago, 1985±1990. Diabetes Care (1995); 18: 476±482. 109. Pinhas-Hamiel O, Dolan LM, Zeitler PS. Diabetic ketoacidosis among obese African-American Adolescents with NIDDM. Diabetes Care (1997); 20: 484± 486. 110. Tull ES, Makame MH, DERI group. Evaluation of Type I diabetes in black African heritage populations: no time for further neglect. Diabetic Med (1992); 9: 513± 521. 111. LaPorte RE, Tajima N, Dorman JS, Cruickshanks KJ, Eberhardt MS, Rabin BS et al. Differences between blacks and whites in the epidemiology of insulin-dependent diabetes mellitus in Allegheny County, Pennsylvania. Am J Epidemiol (1986); 123: 592± 603. 112. Wagenkrecht LE, Roseman JM, Alexander WJ. Epidemiology of IDDM in black and white children in Jefferson County, Alabama, 1979±1985. Diabetes (1989); 38: 629±633. 113. Lorenzie M, Cogliero E, Schmidt NJ. Racial differences in incidence of juvenile-onset Type 1 diabetes: epidemiologic studies in southern California. Diabetologia (1985); 28: 734± 738. 114. Lipman TH. The epidemiology of Type 1 diabetes in children 0 ± 14 years of age in Philadelphia. Diabetes Care (1993); 16: 922± 928. 115. Tull ES, Roseman JM, Christian CLE. Epidemiology of childhood insulin-dependent diabetes mellitus in the US Virgin Islands from 1979± 1988: evidence of an epidemic in the early 1980's and variation by degree of racial admixture. Diabetes Care (1991); 14: 558± 564. 116. MacDonald MJ, Famuyiwa OO, Nwabuelo IA, Bella AF, Junaid TA, Marrari M, Duquesnoy RJ. HLA-DR associations in black Type 1 diabetes in Nigeria: further support for models of inheritance. Diabetes (1986); 35: 583± 589. 117. MacDonald MJ. Speculation on the evolution of insulin-dependent diabetes genes. Metabolism (1988); 37: 1182± 1184. 118. Reitnauer PJ, Go RCP, Acton RT, Murphy CC, Budowle B, Barger BO, Roseman JM. Evidence of genetic admixture as a determinant in the occurrence of insulin-dependent diabetes mellitus. Diabetes (1982); 31: 532± 537. 119. Chakraborty R, Mohammed KI, Nwankwo M, Ferrell RE. Caucasian genes in African Americans. Am J Human Genet (1992); 50: 145± 55. 120. Todd JA, Mijovic CH, Fletcher JA, Jenkins D, Bradwell AR. Identification of susceptibility loci for insulin dependent diabetes by transracial gene mapping. Nature (1989); 338: 587± 589. 121. Garrett TP, Saper MA, BjoÈrkman PJ, Strominger JL, Wiley DC. Specific pockets for the side chains of peptide antigens in HLA-Aw68. Nature (1989); 342: 692± 696.

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122. Platz P, Jakobsen BK, Morling N, Ryder LP, Svjgaard T, Christy M et al. HLA-D and DR antigens in the genetic analysis of insulindependent diabetes mellitus. Diabetologia (1981); 21: 108± 115. 123. Reitnauer PJ, Roseman JM, Barger BO, Murphy CC, Kirk A, Acton RT. HLA associations in a sample of the American black population. Tissue Antigens (1981); 1: 286± 293. 124. Dunston GM, Henry LQ, Christian JQ, Callender CO. HLA DR3, DQ heterogeneity in American blacks is associated with susceptibility and resistance to insulin-dependent diabetes mellitus. Transpl Proc (1989); 21: 653± 655. 125. Wang C, Rivas ML, Burghen GA, Hudson EC, Wyatt RJ. C4 and Bf phenotypes in black and Caucasian patients with childhood onset insulindependent diabetes mellitus. J Clin Lab Immunol (1989); 30: 183± 190. 126. Rodey GE, White N, Frazer TO, Dudquesnoy RJ, Santiago JV. HLA-DR specificities among black Americans with juvenile-onset diabetes. N Engl J Med (1979); 301: 810± 812. 127. Fletcher J, Mijovic C, Odugbesan O, Jenkins D, Bradwell AR, Barnett AH. Trans-racial studies implicate HLA-DQ as a component of genetic susceptibility to type 1 (insulin-dependent) diabetes. Diabetologia (1988); 31: 864± 870. 128. Mijovic CH, Jenkins D, Jacobs KH, Penny MA, Fletcher JA, Barnett A. HLA-DQA1 and -DQB1 alleles associated with genetic susceptibility to IDDM in a black population. Diabetes (1991); 40: 748± 753. 129. Dorman JS, LaPorte RE, Stone RA, Trucco M. Worldwide differences in the incidence of Type 1 diabetes are associated with the amino acid variation at position 57 of the HLA-Dq beta chain. Proc Nat Acad Sci (1990); 87: 7370± 7374. 130. Todd JA, Bell JI, McDevitt HO. HLA-DQ beta gene contributes to susceptibility and resistance to insulin-dependent diabetes mellitus. Nature (1987); 329: 599± 604. 131. Rabb MF, Gagliano DA, Sweeney NE, Diabetic retinopathy in blacks. Diabetes Care (1990); 13: 1202± 1226. 132. Kahn HA, Hiller R. Blindness caused by diabetic retinopathy. Am J Ophthalmol (1974); 78: 58 ± 67. 133. Harris I, Klein R, Cowie CC, Rowland M, ByrdHolt DD. Is the risk of diabetic retinopathy greater in non-Hispanic blacks and Mexican-Americans than in non-Hispanic whites with Type 2 diabetes: a US population study. Diabetes Care. (1998); 21: 1230± 1235. 134. Cruikshank JK, Alleyne SA. Black West Indian and matched white diabetics in Britain compared with diabetes in Jamaica: body mass, blood pressure and vascular disease. Diabetes Care (1987); 10: 170± 179.

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135. Appiah AP, Ganthier R Jr, Watkins N. Delayed diagnosis of diabetic retinopathy in black and Hispanic patients with diabetes mellitus. Ann Ophthalmol (1991); 23: 156± 158. 136. Banerji MA, Berman D, Chaiken RL, Lebovitz HE. Frequency of retinopathy in newly diagnosed diabetes (unpublished data). 137. El-Kebbi IM, Ziemer DC, Gallina DL, Phillips LS. Diabetes in urban African-Americans. Utility of fasting or random glucose in identifying poor glycemic control. Diabetes Care (1998); 21: 501± 505. 138. UK Prospective diabetes study 22. Effect of age at diagnosis on tissue damage during the first 6 years of NIDDM. Diabetes Care (1997); 20: 1435 ± 1441. 139. Held PJ, Pork FK, Webb RL USRDS 1991. Am J Kid Dis (1991); 19 (suppl 2) 1. 140. US Renal Data System: US Renal Data System 1994 Annual Report. Bethesda, MD, National Institute of Health, National Institute of Diabetes and Digestive Diseases, Division of Kidney, Urologic and Hematologic Diseases, 1994. 141. Cowie CC, Pork FK, Wolfe RA, Savage PJ, Moll PP, Hawthorne VM. Disparities in the incidence of end-stage renal disease; according to race and type of diabetes. N Eng J Med (1989); 321: 1074± 1079. 142. Stephens GW, Gillaspy JA, Clyne D, Mejia A, Pollak VE. Racial differences in the incidence of end-stage renal disease in Type I and II diabetes mellitus. Am J Kid Dis (1990); 15: 562± 567. 143. Pugh JA, Medina RA, Cornell JC, Basu S. NIDDM is the major cause of diabetic end-stage renal disease. More evidence from a triethnic community. Diabetes (1995); 44: 1375± 1380. 144. Nelson CB, Port FK, Wolfe RA, Guire KE. Comparison of continuous ambulatory peritoneal dialysis and hemodialysis patients' survival with evaluation of trends during the 1980s. J. Am Soc Nephr (1992); 3: 1147± 1155. 145. Wolfe RA, Port FK, Hawthorne VM, Guire KE. A comparison of survival among dialytic therapies of choice: in-center hemodialysis versus continuous ambulatory peritoneal dialysis at home. Am J Kid Dis (1990); 25: 433± 440. 146. US Renal Data System: USRDS Annual Data Report. Bethesda, MD, National Institute of Health, National Institute of Diabetes and Digestive Diseases, August 1990. 147. Cowie CC, Pork FK, Rust KF, Harris MI. Differences in survival between black and white patients with diabetic end-stage renal disease. Diabetes Care (1994); 17: 681± 687. 148. Brancati FL, Whittle JC, Whelton PK, Seidler Al, Klag MJ. The excess incidence of diabetic endstage renal disease among blacks. JAm Med Assoc (1992); 268: 3079± 3084.

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149. Tierney WM, McDonald CJ, Lutt FC. Renal disease in hypertensive adults: effect of race and Type II diabetes. Am J Kid Dis (1989); 13: 485± 493. 150. Freedman BI, Tuttle AB, Spray BJ. Familial predisposition to nephropathy in AfricanAmericans with non-insulin dependent diabetes mellitus. Am J Kid Dis (1995); 25: 710± 713. 151. Seaquist ER, Goeta FC, Rich S, Barbosa J. Familial clustering of diabetic kidney disease: evidence for genetic susceptibility to diabetic nephropathy. N Engl J Med (1989); 320: 1161± 1165. 152. Petit DJ, Saad MF, Bennett PH, Nelson RG, Knowler WC. Familial predisposition to renal disease in two generations of Pima Indians with Type II (non-insulin dependent) diabetes mellitus. Diabetologia (1990); 33: 428± 443. 153. Acton RT, Bell DSH, Roseman J, Tseng M-L, Louv W. Association of HLA phenotypes with hypertension in African Americans and Caucasoid Americans with Type II diabetes, a population at risk for renal disease. Transpl Proc (1993); 25: 2400± 2403. 154. Goldschmid MG, Domin WS, Ziemer DC, Gallina DL, Phillips LS. Diabetes in urban African Americans. Diabetes Care (1995); 18: 955± 961. 155. Chaiken RC, Palmissano J, Norton ME, Banerji MA, Bard M, Sachmechi I et al. Interaction of hypertension and diabetes on renal function in black NIDDM subjects. Kid Int (1995); 47: 1697± 1702. 156. Dasmahaptra A, Bale A, Raghuwanshi AP, Reddi A, Byrne W, Suarez S et al. Incipient and overt diabetic nephropathy in African Americans with NIDDM. Diabetes Care (1997); 17: 297± 304. 157. Chaiken RL, Khawaja R, Bard M, Eckert-Norton M, Banerji MA, Lebovitz HE. Utility of untimed urinary albumin measurements in assessing albuminuria in black NIDDM subjects. Diabetes Care (1997); 20: 709± 713. 158. Haffner SM, Stern MP, Gruber KK, Hazuda HP, Mitchell BD, Patterson JK. Microalbuminuria potential marker for increased cardiovascular risk factors in non-diabetic subjects. Arteriosclerosis (1990); 10: 72 ± 731. 159. Yudkin JS, Forrest RD, Jackson CA. Microalbuminuria as a predictor of vascular disease in non-diabetic subjects. Lancet (1988); ii: 530± 533. 160. Mogenson C. Glomerular hyperfiltration rate and renal plasma flow in short term and long term juvenile diabetes mellitus. Scand J Lab Invest (1971); 28: 91 ± 100. 161. Mogenson CE, Christiansen CK. Predicting diabetic nephropathy in insulin-dependent patients. N Eng J Med (1984); 311: 89 ± 93. 162. Jones SL, Wiseman MJ, Viberti GC. Glomerular hyperfiltration as a risk factor for diabetic

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nephropathy: five year report of a prospective study. Diabetologia (1991); 34: 59 ± 60. Lervang HH, Jensen S, Brochner-Mortensen, Ditzel J. Early glomerular hyperfiltration and the development of late nephropathy in Type 1 (insulin-dependent) diabetes mellitus. Diabetologia (1988); 31: 723± 729. Herman WH, Teutch SM. Kidney disease associated with diabetes. Diabetes in America. Washington, DC, US Govt Printing Office, NIH Publ no. 85±1468, 1985: pp. 1±319. Damsgaard EM, Mogenson CE. Microalbuminuria in elderly, hyperglycemic patients and controls. Diabatic Med (1986); 3: 430±435. Silveiro SP, Friedman R, Jobim de Azevedo M, Canini LH, Gross JL. Five year prospective study of glomerular filtration rate and albumin excretion rate in normofiltering and hyperfiltering normoalbuminuria NIDDM patients. Diabetes Care (1996); 19: 171± 174. Vedel P, Obel J, Nielsen FS, Bang LE, Svendsen TL, Pedersen OB, Parving HH. Glomerular hyperfiltration in microalbuminuria NIDDM patients. Diabetologia (1996); 39: 1584± 1589. Lebovitz HE, Palmisano J. Cross-sectional analysis of renal function in black Americans with NIDDM. Diabetes Care (1990); 13 (suppl 4): 1186± 1190. Palmissano J, Lebovitz HE. Renal function in black Americans with Type II diabetes. J Diabetic Compl (1989); 3: 40 ± 44. Chaiken RL, Eckert-Norton M, Bard M, Banerji MA, Palmisano J, Sachimechi I, Lebovitz HE. Hyperfiltration in African American Patients with Type 2 diabetes mellitus: cross-sectional and longitudinal data. Diabetes Care: submitted. Center for Disease Control and Prevention. Diabetes Surveillance, 1993. Atlanta, GA, US Department of Health and Human Services, 1993: pp. 87 ± 93. Reiber G, Boyko EJ, Smith DG. Lower extremity foot ulcers and amputations in diabetes In: MI Harris, CC Cowie, MP Stern, EJ Boyko, GE Reiber, PH Bennett (eds), Diabetes in America, 2nd edn. Bethesda, MD, National Diabetes Data Group, NIH Publication No 95 ± 1468, 1995: p 416. Robertson WB, Strong JP. Atheroclerosis in persons with hypertension and diabetes mellitus. Lab Invest (1968); 18: 538± 551. Shafer SQ, Bruun B, Richter RW. Brain infarction risk factors in black New York City stroke patients. J Chron Dis (1990); 27: 127± 133. Otten MW Jr, Tentsch SM, Williamson DF, Marks JS. The effect of known risk factors in the excess mortality of black adults in the United States. J Am Med Assoc (1990); 263: 845 ± 885.

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176. Keil JE, Sutherland SE, Knapp RG, Lackland DT, Gazes PC, Tyroler HA. Mortality rates and risk factors for coronary disease in black as compared to white men and women. N Engl J Med (1993); 329: 73 ± 78. 177. Gillum RF, Grant CT. Coronary heart disease in black populations. II. Risk factors. Am Heart J (1982); 4: 852± 864. 178. Kannel WB, McGee DL. Diabetes and cardiovascular disease: the Framingham Study. J Am Med Assoc (1979); 24: 2035± 2038. 179. Gu K, Cowie CC, Harris MI. Mortality in adults with and without diabetes in a national cohort of the US population, 1971± 93. Diabetes Care (1998); in press. 180. Lowe LP, Liu K, Greenland P, Metzger BE, Dyer AR, Stamler J. Diabetes, asymptomatic hyperglycemia and 22-years mortality in black and white men. The Chicago Heart Association Detection

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9B

Non-Caucasian North American Populations: Native Americans K. M. Venkat Narayan, Robert G. Nelson, Robert L. Hanson, David J. Pettitt and William C. Knowler

National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA

NATIVE AMERICANS Indigenous peoples lived in every region of North America for thousands of years before Europeans arrived. In this chapter, the term Native American will be used to refer to the descendents of these indigenous peoples. Native Americans probably originated from three distinct waves of migration from East Asia across the Bering land bridge (1, 2). The oldest of these migrations, that of the PaleoIndians, is thought to have occurred 16 000 to 40 000 years ago (3, 4). The Paleo-Indians were followed by the Na-Dene about 12 000 to 14 000 years ago (4), and by the Eskimos and Aleuts starting about 9 000 years ago (3, 4). The US government recognizes 325 Native American tribes and 226 Alaskan Native villages while approximately 2.33 million people in the US identify themselves as Native Americans (5). Likewise, Canada recognizes three distinct Aboriginal groups: Indian, Inuit and Metis, and according to the 1991 census just over 1 million Canadians claim `some Aboriginal origin'. In general, Native Americans face widespread economic and educational problems (6).

DIABETES IN NATIVE AMERICANS Diabetes was apparently rare among Native Americans until the middle part of the twentieth century (7, 8, 9). However, since World War II diabetes has become one of the most common serious diseases in many Native American tribes

(10), and in 1987, there were at least 72 000 Native Americans in the US with diagnosed diabetes (11). Diabetes occurring in Native Americans is almost exclusively Type 2 diabetes (10). The Pima Indians of Arizona have participated in a longitudinal study of diabetes, obesity, and diabetes complications since 1965 (12). Data from this study reveal that the Pimas have the highest recorded prevalence and incidence of Type 2 diabetes in the world (13, 14), and that insulin resistance is the major early abnormality in the pathogenesis of Type 2 diabetes (15, 16). High rates of diabetes have also been observed in other Native American tribes (10, 17, 18), and in diverse societies worldwide that have recently adapted to Western culture (19 ± 21). People of Pima heritage living in Mexico may have a lower prevalence of obesity and Type 2 diabetes than their counterparts in Arizona, US, possibly due to lifestyle factors (22).

MAGNITUDE OF THE PROBLEM OF TYPE 2 DIABETES IN NATIVE AMERICANS Prevalence Estimates of prevalence are influenced by the method of ascertainment and by the definition of diabetes. A large number of studies have reported the prevalence of clinically diagnosed diabetes among Native Americans in the US (23 ±26). The prevalence of diagnosed diabetes varies across tribes and is, generally, higher than in the US population (23 ± 26). The age-adjusted rate of

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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clinically diagnosed diabetes among all Indian Health Service (IHS) patients was 6.9%, which was nearly three times the US all-races rate, and of the 11 IHS areas examined in this study, only the Alaska area had a lower prevalence than the allraces US rate (26). However, there are indications that the rates of diagnosed diabetes among Alaska Natives may also be increasing (27). In Canada, one study reported the age-standardized prevalence of diagnosed diabetes among Aboriginal people ranging from 0.18% among Chukchi and Eskimo of Chukota, 0.79% among Eskimo=Inuit of Alaska, 1.86% among Athapaskan Indians in Alaska, to 2.27% among Aleuts in Alaska (28). Another study among aboriginal Canadians reported age-sex-adjusted rates varying from 0.8% in the Northwest Territories to 8.7% in the Atlantic region (29). In the Canadian Aboriginal Peoples Survey of 1991, the overall prevalence of self-reported diabetes in Aboriginal people was 6.0, and varied from 1.9% in Inuit (Eskimo), 5.5% in Metis, to 6.4% in Indians (30). Studies based on clinically diagnosed cases rather than systematic testing may underestimate the prevalence of diabetes because a large proportion of Type 2 diabetes may remain undiagnosed (31). Prevalence studies of diabetes in Native Americans based on systematic testing in the community are available for only a few tribes (32 ± 35), and are summarized in Table 9B.1. Among the studies reported in Table 9B.1, only Lee et al. (32) estimated the prevalence based on

systematic glucose tolerance testing and classification by WHO (36). The other three studies (33 ±35) used a variety of ascertainment methods, and did glucose tolerance testing only on subjects meeting certain criteria on other tests. In a populationbased study among Algonquin communities in Quebec, the age ±sex standardized prevalence of Type 2 diabetes among people æ15 years old was 19.1% in Lac Simon and 9% in River Desert (37). In the same two communities, the prevalence among 30 ±64 year olds was 48.6% in women and 23.9% in men (38). Mexican Americans in the US also have high rates of diabetes, and their prevalence ranges from 3.8% among 20 ± 44 year olds to 23.9% among 45 ± 74 year olds (39). Overall, the prevalence of diabetes in Native Americans is higher than the rate of 6.6% in the US general population (31). An epidemiological study that compared the Pima Indians with a predominantly white population of Rochester, Minnesota, gives further evidence that Native Americans have a higher prevalence of diabetes (13). Age-sex standardized diabetes prevalence for Pimas was 12.7 times that of Caucasians, and by contrast, diabetes prevalence in Rochester was higher in men than in women. The prevalence of diabetes in the Pima Indians, has increased during three successive decades (Figure 9B.1) (40). Overall, the prevalence of diabetes increased by 29% in men between 1965 ± 74 and 1985 ± 94, and by 35% in women during the same period.

Table 9B.1. Age-adjusted prevalence of diabetes in Native Americans from population-based studies Author

Lee (32)

Rith-Najarian (33) * Sugarman (34) * Hall (35) * *

Study population

Men and women aged 45±74 yrs: Pima=Maricopa=Papago, Arizona Apache, Caddo, Comanche, Delaware, Fort Sill Apache, Kiowa, Wichita, Oklahoma Oglala, Sioux, Cheyenne River Sioux, Devils Lake Sioux, North and South Dakota Men and women of all ages, Red Lake Chippewa Indians Men and women aged 20±74, Navajo Indians, Shiprock Men and women aged æ20 years, Navajo Indians, Many Farms-Rough Rock

Prevalence * M (%)

F (%)

T (%)

65 38

72 42

70 40

33

46

40

13 14 11

16 18 14

15 17 12

* Prevalence rates are standardized to US general population for the relevant ages. * * These studies used a variety of ascertainment methods, and did glucose-tolerance testing only on subjects meeting certain criteria on other tests.

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183

Figure 9B.1 Age± sex-specific prevalence of diabetes in Pima Indians in three time periods. Prevalence rates were estimated from data from all subjects examined in each of the 10-year periods 1965±74, 1975±84, and 1985±94 Source: Updated from data in reference (40)

Why is the prevalence of diabetes increasing? Prevalence can increase for two reasons: improvement in survival and=or increase in the rate of development of new cases. The length of survival following the onset of diabetes may have increased

over time, due to better treatment or due to a change in the natural history of the disease. However, diabetes has contributed little to mortality rates in Pimas under the age of 55 years (41), and therefore, an improvement in survival is

Figure 9B.2 Age± sex-specific incidence rates of diabetes in Pima Indians during three decades. Incidence rates are expressed as new cases of diabetes per 1000 person-years of observation of non-diabetic subjects. Cases and person-years are divided into three time periods: 1965±74, 1975±84, and 1985±94 Source: Updated from data in reference (40)

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an unlikely explanation for the increase in prevalence among younger Native Americans. This suggests that at least part of the increase in prevalence is due to an increase in the incidence of the disease. Incidence The Pimas have the highest reported incidence of diabetes in the world Ð 19 times the rate of diagnosed diabetes in the predominantly white population of Rochester, Minnesota (13) Ð and high incidence of diabetes has also been reported in other Native American tribes in the US (33), and Canada (42). Figure 9B.2 shows the age-sexspecific incidence of diabetes in Pima Indians during three successive decades. The incidence rates vary by age, peaking between 35 and 44 years in men in 1965 ± 74, and 45 and 54 years in men in more recent years. Among women, the incidence rates peaked between 45 and 54 years in 1965± 74 and in 1985± 94, and 55 and 64 years in 1975 ±84. The incidence of diabetes has also increased over three successive decades at most ages and in both men and women. Overall, the age-adjusted incidence increased by 102% in men and by 87% in women between 1965± 74 and 1985± 94. Why is the incidence of diabetes increasing? The dramatic increase in incidence of diabetes over a relatively short period of time emphasizes the overriding importance of environmental determinants among persons with an underlying genetic susceptibility.

more likely to transmit diabetes to an offspring than is a parent with an older age of onset (Figure 9B.3) (40). Thus, individuals who develop diabetes at younger ages may have a greater `load' of diabetes susceptibility genes than those who develop the disease later in life. Similarly, the prevalence of Type 2 diabetes is higher in relatives of leaner diabetic Pimas than in relatives of heavier individuals with the disease (48), suggesting that the genetic factors which result in Type 2 diabetes are at least partially separate from those that cause obesity. While familial aggregation of a disease suggests the potential importance of genetic factors, segregation analysis can determine whether this aggregation is consistent with a particular mode of inheritance. Among Seminoles, analyses of 1 h post-load glucose concentrations were consistent with the hypothesis that a single genetic locus has a major effect on these levels, but the degree of dominance at this putative locus could not be determined (49). Among Pimas, segregation analyses were consistent with a major effect of a single locus influencing age of onset of disease (50). On the basis of these analyses, it is tempting to speculate that familial aggregation of Type 2 diabetes among Native Americans may be explained in large part by the action of a single genetic locus. More precise knowledge of the genetics of Type 2 diabetes would be obtained if a particular genetic

DETERMINANTS OF TYPE 2 DIABETES Genetic Factors Genetic factors may be important determinants of Type 2 diabetes in Native Americans. The prevalence of Type 2 diabetes is higher in individuals of full Native American heritage than in those with genetic admixture (32, 43 ±45), possibly because full heritage Native Americans have a greater dose of diabetes susceptibility genes than admixed individuals. Familial aggregation of diabetes occurs in several Native American populations (32, 46, 47). For example, among Pimas, a parent who developed diabetes at a younger age is

Figure 9B.3 Prevalence of diabetes by presence and age of onset of diabetes in parents. Diabetes was defined as of the last examination in the parents. Parents whose onset of diabetes was before age 45 were classified as `early' Source: Updated from data in reference (40)

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marker was strongly associated with or linked to the disease. Among Pimas, there is a modest association of Type 2 diabetes with HLA-A2 phenotype (51) and also with alleles at the glycogen synthase locus (52). Tentative evidence of linkage to Type 2 diabetes has also been observed with markers on chromosomes ly, 7y and diabetes and obesity was linked to 11y (53). Tentative evidence of linkage of the intestinal fatty acid binding protein with insulin resistance, an important physiologic abnormality underlying Type 2 diabetes, has also been observed in Pimas (54); this locus may also be linked to 2 h post-load insulin levels in Mexican Americans (55). At present, the significance of these findings with respect to the etiology of Type 2 diabetes in Native Americans remains unclear. Further research into the genetics of Type 2 diabetes will, hopefully, lead to a better understanding of the pathogenesis of the disease. Perinatal Factors Among the Pimas, perinatal factors have an influence that modifies their genetic predisposition. Rates of diabetes are higher among subjects who were of very low or very high birthweight, whose mothers had diabetes during the pregnancy, or who were not breastfed. As mentioned above, diabetes is a familial disease, occurring more frequently in those with a diabetic parent, whether that parent be the father or the mother. However, the diabetic intrauterine environment presents a risk for the early development of diabetes in Pima Indians which is in addition to the genetic predisposition (56). Before the age of 10 years, Type 2 diabetes is virtually limited to those children whose mothers had diabetes during the pregnancy, and half of the offspring of diabetic pregnancies have already developed diabetes by the time they reach childbearing age. This results in a vicious cycle of diabetes begetting diabetes (57). Higher rates of diabetes are found among adults who were at the extremes of birthweight (58). Rates of diabetes among those with birthweights below 2.5 kilograms or over 4.5 kilograms are nearly twice as high as among those with intermediate weights. This very likely represents selective survival of those low-birthweight infants with a predisposition to Type 2 diabetes.

Breastfeeding for a period of at least 2 months is associated with a 50% reduction in rates of diabetes (59). The reasons for this have not been fully explored, but may relate to a nutritional intake more suited to an infant's growing needs, either because of the differences in breast- and bottlefeeding per se or because mothers who choose to breastfeed continue to feed their children differently than those who do not. Environmental Factors A number of factors, which are potentially modifiable, including obesity, dietary composition, and physical inactivity are thought to contribute to the progression from genetic susceptibility to Type 2 diabetes (60 ± 62). Obesity Obesity is a powerful and well-established risk factor for the development of Type 2 diabetes (46). As shown in Figure 9B.4 the age±sex-adjusted incidence of diabetes in Pima Indians increases with body mass index (BMI), a measure of obesity. Furthermore, the incidence of diabetes increases with the duration of obesity (BMI æ30 kg=m2), and

Figure 9B.4 Age± sex-adjusted incidence of diabetes in Pima Indians by body mass index (BMI), with 95% confidence intervals Source: Updated from data in reference (40)

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compared with Pima Indians with less than 5 years of obesity, those with 5±10 years of obesity have 1.4 times the incidence of Type 2 diabetes, and those with at least 10 years of obesity have 2.4 times the incidence (63). The distribution of body fat may also be important, and central obesity is related to the risk of diabetes (64±66). Those who gain weight most rapidly are most likely to develop diabetes, whereas those who lose weight are at the lowest risk (67). As seen in Figure 9B.5, the prevalence of obesity among Native Americans is higher than in the US general population in both males and females and at all ages (68). The overall prevalence of obesity (BMI æ31.1 kg=m2 for men, æ32.3 kg=m2 for women) among Native Americans was 13.7% for men and 16.5% for women, higher than the US rates of 9.1% and 8.2% respectively (68). Data from the Pimas are consistent with this finding. Furthermore, the mean BMI in Pima adults has increased over time (Figure 9B.6), and a secular increase in the prevalence of overweight has also been reported in the Navajo Indians (35). Pima children have also, on average, become heavier during this century, and continue to do so (64). Diet Diet has been linked to the development of diabetes for over 2500 years (69), but the precise

Figure 9B.6 Mean BMI in Pimas for two periods and in the US, white population. The Pima data from each period were used for all subjects examined in each of the 8-year periods 1965± 72 and 1981±88 Source: Updated from data in reference (64). The US data are from NHANES II (1976 ± 1980)

role of dietary factors, which has been reviewed elsewhere (70), remains ambiguous. Few data are available in Native Americans linking dietary factors with the development of Type 2 diabetes, except for one study in the Pima Indians which found that a high calorie diet may be associated with diabetes (71). The traditional Pima diet, derived from local agricultural produce, is believed to have been high in fiber, and low in fat (40), but the Pima diet changed during this century and is now nutritionally similar to the diet in the rest of the US (72). Similar secular changes in the diet of other Native American populations have also occurred, and in particular, the fat content of Native American diets appears to have increased dramatically Ð from 17% of total calories in the pre-European contact diet to 38% in the current diet (73). Physical Activity

Figure 9B.5 Age±sex-specific prevalence of obesity (BMI æ31.1 kg=m2 for men, æ32.3 kg=m2 for women) in Native Americans and US all races, 1987 Source: Adapted from data in reference (68). Data source: National Medical Expenditure Survey (NMES)

Increased physical activity may have a protective effect on the development of Type 2 diabetes (61). In Pima Indians the age-adjusted prevalence of Type 2 diabetes in 15±36-year-old subjects was lower in those with higher amounts of leisure physical activity in the preceding year (Figure 9B.7).

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187

Vascular Complications

Figure 9B.7 Age-adjusted prevalence of Type 2 diabetes and 95% confidence interval by tertile groups of past year leisure physical activity in Pimas aged 15±36 (upper panel) and 37±59 years (lower panel)

Nearly all of the excess mortality associated with either type of diabetes is found in persons with proteinuria (76±78). Figure 9B.8 shows the age± sex-adjusted death rate among Pima Indians according to the presence or absence of diabetes and proteinuria. The death rate in diabetic subjects without proteinuria was no greater than in nondiabetic subjects, but the rate in those with clinical proteinuria was nearly 4-fold higher. The excess deaths in diabetic subjects with proteinuria are due principally to cardiovascular or renal disease (77±79), leading to the inference that proteinuria reflects widespread vascular damage in both small and large vessels (80). The frequency with which proteinuria leads to life-threatening cardiovascular disease or to renal failure in persons with diabetes depends on the frequency of other risk factors for these diseases. For example, persons in whom Type 2 diabetes develops later in life may have a higher risk of death from cardiovascular disease than persons in whom diabetes develops at a younger age, because of greater exposure to risk factors for cardiovascular disease that precede the onset of diabetes (81). Conversely, persons who develop Type 2 diabetes at a younger age may be more prone to develop end-stage renal disease, particularly if their risk of cardiovascular disease is low (82).

Source: From data in reference (74)

Among 37±57-year-old subjects, those with the lowest levels of physical activity also had the highest prevalence of diabetes (74). Similarly, Zuni Indians with diabetes were less likely to have exercised frequently than were those without (75). COMPLICATIONS OF TYPE 2 DIABETES The frequency of several important vascular and non-vascular complications of Type 2 diabetes in Native Americans is examined, with particular emphasis on those that exert a significant influence on mortality.

Figure 9B.8 Age± sex-adjusted death rates and 95% confidence intervals in 1426 Pima Indians æ45 years of age. Rates are shown for non-diabetic subjects without proteinuria (Nondiab), diabetic subjects without proteinuria (Diabetic), and diabetic subjects with proteinuria (Diab ‡ Prot). Proteinuria was defined by a protein-to-creatinine ratio æ1.0 g=g Source: From data in reference (78)

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Although a rise in albumin excretion characteristically occurs after the onset of Type 2 diabetes, it may also precede Type 2 diabetes. The prevalence of elevated urinary albumin excretion (albumin-tocreatinine ratio æ30 mg=g) is twice as high in Pima Indians with impaired glucose tolerance as in those with normal glucose tolerance (Figure 9B.9) (83). This suggests that even small elevations of the plasma glucose concentration may have an impact on vascular function. With the onset of Type 2 diabetes, the prevalence of elevated urinary albumin excretion is even higher, and both the magnitude and frequency of the elevation are related to the duration of diabetes. Among Pima Indians with diabetes of up to 5 years duration, the prevalence is 29%, and in those with diabetes æ20 years it is 86% (Figure 9B.9). Microalbuminuria (albumin-tocreatinine ratio =30± 299 mg=g) accounts for 82% of the prevalent cases among Pima Indians with 1.0%; of these, three studies were those from the same country, i.e. South Africa (Table 13.1). Over the past 20 years and after the introduction of the WHO criteria (3, 4), there have been several reports from West Africa (8 ±14), East Africa (15, 16), North Africa (17 ± 20) and South Africa (21 ± 23) (Table 13.2). Prevalence rates range from an absence of diabetes mellitus in Togo to higher and moderate rates in Cape Town in South Africa (8%) and Egypt in North Africa (9.3%).

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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Table 13.1 Prevalence of diabetes mellitus in Africa: studies done before 1980=1985 WHO criteria * Country

Author

Year

Urban= rural * *

Study population

Ethiopia Ghana

Peters Dodu Dodu & de Heer Zmirou Politzer et al. Davidson Imperato et al. Seftel & Abrams Politzer & Schneider Goldberg et al. Goldberg et al. Marine et al. Tulloch Davidson et al. Corr & GeÁlfand Wicks et al. Guidotti & GeÁlfand

1983 1958 1964 1979 1960 1963 1976 1960 1962 1969 1969 1969 1964 1969 1961 1973 1976

U=R U U U=R R U=R U=R U U U U U U=R R U U R

Community‡ Outpatients Community‡ Community Outpatients Outpatients Community‡ Outpatients Outpatients Community Community Community Outpatients Community Community Community Community

Ivory Coast Lesotho Malawi Mali South Africa

Uganda Zambia Zimbabwe

Sample size 2381 4000 5537 5000 3000 4725 297 2122 3121 882 2015 1029 7164 369 107 1078 5456

Detection method Urine Urine Urine Blood Urine Urine Blood Urine Urine Urine=blood Urine Urine=blood Urine Blood Urine Urine Urine

Prevalence (%) 0.3 0.4 0.2 5.7 0.2 0.1 1.4 1.3 0.6 2.7 2.9 3.6 0.2 1.1 0.1 0.3 0.0

* Adapted from McLarty et al. (1). * * U = urban; R = rural Selected community: Ethiopia: Schools=factories, Ghana: Males >15 yr; Mali: police=farmers=students

‡

In sub-Saharan Africa, the prevalence of diabetes is low in both rural and urban communities in countries in West Africa and in Tanzania in East Africa. By contrast, moderate rates (5 ±8%) have been reported from South Africa, in a semiurban community in the Orange Free State (OFS) (4.8%) and in urban groups in OFS (6%), Durban (Zulu) (5.3%) and Cape Town (Xhosa) (8.0%). Studies from North Africa indicate moderate prevalence rates in Sudan and Tunisia (3.4±8.3%). In an Egyptian study, overall prevalence was 9.3% and high rates (13.5%, 20%) were reported in urban populations in Cairo. It is important to note that there were differences in the methodology in the various studies in terms of age group studied, the use of fasting blood glucose (FPG) alone, and the use of reflectance meters as opposed to formal laboratory blood glucose estimations. From the evidence to date, it would appear that in sub-Saharan Africa north of the Limpopo River, diabetes prevalence is low in both urban and rural communities. However, in South Africa and North Africa, moderate and high rates are found and do not differ significantly from rates found in developed countries; urban Egyptians in fact show a high prevalence. Moreover, there appears to be a difference in urban and rural

prevalence. What is also highlighted is the relative paucity of information on diabetes epidemiology in Africa and the need for further studies, both urban and rural. IMPAIRED GLUCOSE TOLERANCE; TOTAL GLUCOSE INTOLERANCE; EPIDEMICITY INDEX The combined prevalence of diabetes (D) and impaired glucose tolerance (IGT), i.e. total glucose intolerance (TGI), may serve as a useful measure of the public health impact of glucose intolerance in a given population (5, 24). This is because IGT may indicate a high risk of subsequent diabetes development. Moreover, it has been suggested that the percentage of TGI made up by IGT, i.e. `Epidemicity Index' or the ratio of IGT to Diabetes (IGT:D) or TGI (IGT:TGI), may have some predictive value in determining the stage of an epidemic of glucose intolerance in a given population, i.e. a high IGT prevalence in the face of a low prevalence of diabetes (high IGT:D ratio) may indicate an early stage of a diabetes epidemic. Table 13.3 shows the prevalence of IGT and total glucose intolerance (TGI) (diabetes and IGT)

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Table 13.2 Prevalence of diabetes mellitus (D) and impaired glucose tolerance (IGT) in Africa: studies published after introduction of 1980=1985 WHO criteria Country locality

Authors

Tanzania Kahalanga Ndolage Mwanza Mali Togo Nigeria

AhreÂn & Corrigan (15)

1984

Tunisia Tunis Siliana Tanzania

Fisch et al. (8) Teuscher et al. (9) Ohwovoriole et al. (10) Erasmus et al. (11) Papoz et al. (17)

1987 1987 1988 1989 1988

McLarty et al. 916)

1989

U R R

South Africa Cape Town

Levitt et al. (22)

1993

Omar et al. (21)

1993

Mollentze et al. (23)

1995

Herman et al. (18)

1995

Ducorps et al. (13) Mbanya et al. (14)

1996 1997

Elbagir et al. (19)

1996

Cooper et al. (12) Elbagir et al. (20)

1997 1988

Durban Orange Free State Mangaung Qwa-Qwa Egypt Cairo Kaliubia Mauritania Cameroon Yaounde Evodoula Sudan Nigeria Sudan

Year

Urban (U) rural (R)

Age group (yr)

IGT

0.7 0.5 2.5 1.9 0.9 0.0 1.7 2.4

± ± ± ± ± ± ± ±

3.8 1.3 0.9 1.1 *

± ± 7.8 8.4 *

6.3 8.0 * 4.2 5.3 *

5.9 7.0 * 6.9 7.7 *

758 853 1451 213 734 504 744 1767 1048 719 1284 826 458

6.0 * 4.8 * 9.3 20.0 13.5 4.9 1.88 1.1 1.3 0.8 3.4 3.9 2.6

12.2 * 10.7 * 9.6 8.6 5.4 13.1 ± 2.7 1.8 3.9 2.9 3.3 2.2

247 724

2.8 8.3 10.4 * 9.6 6.1

± 7.9 9.8 * 8.0 7.6

æ15

U

>30

729

U

>15

479

U R U‡R U-h * * U-l * * R U‡R U‡R U R U‡R U R R U‡R U R

>15 >1 æ20

æ25 æ20

>17 24±74 æ25

25±74 æ25

Prevalence (%) D

3145 996 1141 1008 7472 1381 1627 2800 5613 3826 1787 6097

U‡R R R U R R U U‡R

æ20

n

461 263

* Age-adjusted rates. * * U-h = urban, high socio-economic status; U-l = urban, low socio-economic status.

in the eight studies in which it was examined. IGT prevalence ranges from 2.2% in rural Sudan to 13.1% in rural Egypt, and TGI from 4.8% in rural Sudan to 28.6% in urban Egyptians from a high socio-economic status group. It is apparent that in Africa, as in the rest of the world, where the prevalence of diabetes is low (> 0% < 3%), the IGT rates are higher. This is shown in rural Tanzanians in whom the prevalence of diabetes and IGT is 1.1 and 8.4%, respectively

(16). By contrast, in high diabetes prevalence (>10%) populations, the IGT rates are lower, e.g. in urban Egyptians in whom the prevalence of diabetes and IGT is 20.6% and 8.6%, respectively (18). The Epidemicity Index (percentage of TGI made up by IGT) decreases as the diabetes prevalence increases. This is exemplified in the low (10%) diabetes prevalence (20.6%), the Epidemicity Index is 30% (18). Clearly, there is a need for further studies in Africa to evaluate the significance of IGT with respect to its natural history and its usefulness as a marker of diabetes epidemicity in different populations. URBAN=RURAL DIFFERENCES The prevalence of diabetes in rural and urban communities in the same country has been examined in a few studies (Tables 13.2, 13.3) (17, 15, 19, 20, 23, 18, 14). In all but one country, the prevalence was higher in urban communities. The difference was most striking in the Egyptian study in which the urban prevalence rates (13.5%; 20%) were 2 ±4-fold higher than rural rates (4.9%) (18). The results from the Tanzanian study were conflicting; when compared with the urban prevalence (1.9%), the rate was lower in the rural Sukuma tribe (0.5%) but higher in the rural Haya tribe (2.5%). The

possible reasons for the differences between the two rural groups include varying age and gender distribution, genetic and lifestyle factors. In South Africa, Sudan and Cameroon, although the diabetes frequency was higher in urban groups, the difference was not marked. In South Africa, this might be accounted for by the fact that in recent years, urbanization and industrialization have affected even the so-called `rural' areas; it has been estimated that in the new millennium, >75% of the country's population will be urbanized (23). The effect of urbanization as a risk factor for diabetes was examined in studies from South Africa and Sudan (2, 19, 20). While urbanization was not a risk factor in the Sudanese studies, in urban Xhosas in Cape Town, South Africa, urbanization as judged by the proportion of life spent in the city (cutoff level >40%) was a significant risk factor. However, the definition and measurement of urbanization is under considerable debate. ETHNIC DIFFERENCES Ethnic differences were examined in Tanzania and South Africa (25, 21, 26). In both countries,

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diabetes prevalence was lower in the indigent African population than in the migrant Asian group (African vs Asian; 5.3 vs 13% in South Africa, 1.1 vs 9.1% in Tanzania). Regarding IGT, in South Africa, rates were higher in Africans (7.7%) than in Asians (6.9%); by contrast, in Tanzania, the prevalence was lower in Africans (8.4%) than in Asians (16.2%).

this was true also for the rural population in which >45% were known to have the disorder. In earlier studies from South Africa, the proportion of African subjects with known diabetes ranged from 28 to 33% and compared with that found in the Europid population in that country (1).

PREVALENCE OF KNOWN DIABETES

The ratio of the diabetes prevalence in men and women varies within and between populations, with no obvious trend. A male preponderance is reported from studies in Tanzanians (rural), Tunisians, Sudanese (urban), Egyptians (urban) and Cameroon (rural) (16, 17, 20, 18, 14). A female excess was found in studies from South Africa (Durban), Mauritania, Mali, Cameroon (urban), Sudan (rural), Egypt (rural, urban) and Tanzania (rural) (21, 13, 8, 14, 20, 18, 15). An equal gender distribution was observed in South Africa (Cape Town), Tanzania (urban) and Sudan (22, 15, 19).

The proportion of subjects with known diabetes was lowest in studies in Tanzania; this applied even to the study which included an urban sample (15, 16) (Table 13.4). By contrast, >50% were known to have diabetes in studies from Tunisia (17, 18), Egypt (18) and South Africa (22, 21) which compares with findings in developed countries (5); moreover, these figures may reflect the better access to health care facilities and therefore greater opportunistic screening in these countries. In Tunisia and Egypt,

GENDER DISTRIBUTION

Table 13.4 Prevalence of known and newly discovered subjects with diabetes mellitus from population-based studies in Africa Country * locality

Authors

Year

Tanzania (U ‡ R) (R) Tunisia Tunis (U) Siliana (R) South Africa Durban (U) Cape Town (U) Sudan (U‡R) (U) (R) Sudan (U‡R) U R Egypt Kaliubia (R) Cairo (U-h) * * (U-l) * * Cameroon (U‡R) Yaounde (U) Evodoula (R)

AhreÂn & Corrigan (15) McLarty et al. (16) Papog et al. (17)

1984 1989 1988

Omar et al. (21) Levitt et al. (22) Elbagir et al. (19)

1993 1993 1996

Elbagir et al. (20)

1998

Herman et al. (18)

1995

Mbanya et al. (14)

1997

* U = urban; R = rural * * U-h = urban, high socio-economic status; U-l = urban, low socio-economic status.

Diabetes mellitus Total n

Known %(n)

Discovered % (n)

22 53

4.6 (1) 13.2 (7)

95.4 (21) 86.8 (46)

144 24

59.7 (86) 45.8 (11)

40.3 (54) 54.2 (13)

20 46 44 32 12 60 44 16

60.0 52.2 36.4 37.5 33.3 38.3 34.1 50.0

40.0 47.8 63.6 52.5 6.7 61.7 65.9 50.0

20 14 6

40.0 (8) 42.9 (6) 33.3 (2)

(12) (24) (16) (12) (4) (23) (15) (8)

(8) (22) (28) (20) (8) (37) (29) (8)

60.0 (12) 57.1 (8) 66.7 (4)

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The lack of discernible trend in gender distribution accords with findings from global estimates (5). However, in that report the apparent male preponderance in Africa is probably accounted for by the fact that only the Tunisian and Tanzanian data were included.

IMPACT OF AGE As in other regions of the world, using age-specific rates, the prevalence of diabetes in Africa increases with age, both in men and women. From global estimates, King et al. (5) report that in moderate± high risk populations (diabetes prevalence >3%), peak prevalence occurs in the sixth decade with a decline in the seventh decade, probably because of greater mortality amongst diabetes subjects. But, in most African studies which report moderate diabetes prevalence, the peak prevalence is in the seventh decade (17, 19, 22, 21); this was true even for studies in Tanzanians and Mauritanians in whom there is a low diabetes prevalence (15, 16, 13). A sixth decade peak was observed in Durban, South Africa (women), OFS, South Africa (urban men) and Sudan (men and women) (21, 23, 20). When compared with other known risk factors, age was found to be a significant risk factor for diabetes in Cape Town, South Africa and Sudan (22, 19, 20).

BODY MASS INDEX AND WAIST ±HIP RATIO In most studies, associations, albeit variable, have been found between body mass index (BMI) and diabetes prevalence. However, the lack of standardized reporting methods makes comparison between studies difficult. In the main, though, the four aspects which have been examined include BMI-specific diabetes prevalence, mean BMI and obesity prevalence in different glucose tolerance categories and BMI=obesity as risk factors for diabetes. Using BMI-specific rates, it has been shown that diabetes prevalence increases with BMI in rural populations in Mali, Tanzania and Nigeria (8, 16, 12).

When compared with non-diabetic subjects, the mean BMI was higher in diabetes subjects in Mali, Tunisia, Sudan, Egypt and Durban, South Africa (8, 17, 19, 18, 21). The prevalence of obesity was higher in diabetic than in non-diabetic subjects in studies from Tanzania, Sudan and Durban, South Africa (15, 19, 21). Obesity was reported in 9.1% of diabetes subjects in Tanzania and in 65% of such subjects in the urban South African Zulu population in Durban (15, 21). Of interest, was that in the latter group, obesity was prevalent even in women with normal glucose tolerance (31.8%); it is therefore possible that the higher obesity prevalence in women could account for the female diabetes preponderance in this group. However, this was not borne out by the results in urban Xhosas in Cape Town, South Africa, where although the mean BMI of the study women was high (30.9 kg=m2) and higher than in men (24.2 kg=m2), there was no female preponderance (22). BMI (23, 19, 20, 12) and obesity (22) were found to be significant risk factors for diabetes in all the studies in which they were examined. Waist ±hip ratio (WHR) and upper segment fat distribution (USFD) have been examined in four studies (22, 23, 12, 18). The prevalence of diabetes increased with WHR in the small rural Nigerian group and in the urban Cape Town Study in South Africa. In the Egyptian study, mean WHR was higher with worsening of glucose tolerance (D > IGT > NGT). USFD was found to be a significant risk factor for diabetes in two studies from South Africa (Cape Town, Orange Free State) (22, 23). PHYSICAL ACTIVITY This has only been examined in two studies (22, 18). In the South African study from Cape Town, physical activity was not found to be a significant risk factor for diabetes. By contrast, in the Egyptian study, the prevalence of diabetes was inversely related to physical activity. FAMILY HISTORY The impact of family history of diabetes has been reported in studies from South Africa and Sudan

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AFRICA

(22, 19, 20). A positive family history was not a risk factor for diabetes in urban Xhosas in Cape Town, South Africa. In two Sudanese studies, a positive family history was more frequent in diabetes subjects and was found to be a significant risk factor for diabetes. IS THE PREVALENCE OF DIABETES INCREASING IN AFRICA? The paucity of data from earlier studies in the same population as well as different diagnostic criteria employed make it difficult to answer such a question. Notwithstanding these limitations, studies in urban Xhosas in Cape Town, South Africa, suggest that the prevalence of diabetes has increased over the last 15 years. Using 1985 WHO criteria in subjects >30 years, the crude prevalence of diabetes is 6.3% and of IGT 5.9% (total glucose intolerance 12.2%) (22). In 1969, Marine et al. (see [1]) used less stringent criteria which probably included IGT as well (`diabetes' was diagnosed if two of three of the following were increased: fasting blood glucose >6.6 mM; 1 h post-glucose value >11.1 mM; 2 h post-glucose value >7.6 mM); in that study, the crude prevalence of `diabetes' in subjects >15 years was 3.6% and in subjects >35 years was 7%. Therefore the prevalence of abnormal glucose tolerance appears to have almost doubled in 20 years. Whether the difference could be explained solely by the degree of urbanization is unclear. LONGITUDINAL STUDIES To date, there are no reports of longitudinal studies which have examined the incidence of diabetes or the natural history of intermediate stages of glucose intolerance (IGT, IFG) in Africa. CONCLUSION=SUMMARY Data on the epidemiology of Type 2 diabetes mellitus in Africa are limited. However, contrary to a commonly held belief, diabetes is not rare in this continent. Although the prevalence is low in some rural populations, in other countries, moderate rates have been found in both urban and rural

communities and are comparable with those in developed countries; in yet others alarmingly high rates are reported. The moderate-to-high prevalence of impaired glucose tolerance (IGT), especially in populations with a low diabetes prevalence, is a possible indicator of the early stage of diabetes epidemic. Diabetes prevalence is higher in urban populations; the putative role of urbanization and industrialization needs to be established. Although there is evidence for a significant association with modifiable risk factors, there is a dearth of data on the impact of such variables as dietary and genetic factors and the role of insulin. Thus there is a need for further studies which use standardized criteria and reporting methods.

REFERENCES 1. McLarty DG, Pollitt C, Swai ABM. Diabetes in Africa. Diabetic Med (1990); 7: 670± 684. 2. National Diabetes Data Group. Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes (1979); 28: 1039± 1057. 3. World Health Organization. WHO Expert Committee on Diabetes Mellitus. Second Report. Geneva, WHO (Tech. Rep. Ser., no. 626), 1980. 4. World Health Organization. Diabetes mellitus: Report of a Study Group. Geneva, WHO (Tech. Rep. Ser. no. 727), 1985. 5. King H, Rewers M, WHO Ad Hoc Diabetes Reporting Group. Global estimates for prevalence of diabetes mellitus and impaired glucose tolerance in adults. Diabetes Care (1993); 16: 157± 177. 6. Amos AF, McCarty DJ, Zimmet P. The rising global burden of diabetes and its complications: estimates and projections to the year 2010. Diabetic Med (1997); 14(suppl 5): S1± S85. 7. King H, Aubert RE, Herman WH. Global burden of diabetes, 1995± 2025. Prevalence, numerical estimates and projections. Diabetes Care (1998); 21: 1414± 1431. 8. Fisch A, Pichard E, Prazuck T, Leblanc H, Sidibe Y and BruÈcker G. Prevalence and risk factors of diabetes mellitus in the rural regions of Mali (West Africa): a practical approach. Diabetologia (1987); 30: 859± 862. 9. Teuscher T, Rosman JB, Baillod P, Teuscher A. Absence of diabetes in a rural West African population with a high carbohydrate=cassava diet. Lancet (1987); i: 765±768. 10. Ohwovoriole AE, Kuti JA, Kabiawu SIO. Casual blood glucose levels and prevalence of undiscovered diabetes mellitus in Lagos Metropolis Nigerians. Diabetes Res Clin Pract (1988); 4: 153±158.

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11. Erasmus RT, Fakeye T, Olukoga, Okesina AB, Ebomoyi E, Adeleye M, Arije A. Prevalence of diabetes mellitus in a Nigerian population. Trans R Soc Trop Med Hyg (1989); 83: 417 ±418. 12. Cooper R, Rotimi C, Kaufman J, Owoaje E, Fraser H, Forrester T, Wills R, Riste L, Cruickshank J. Prevalence of NIDDM among populations of the African diaspora. Diabetes Care (1997); 20: 343±348. 13. Ducorps M, Baleynaud S, Mayaudon H, Castagne C, Bauduceau B. A prevalence survey of diabetes in Mauritania. Diabetes Care (1996); 19: 761±763. 14. Mbanya JC Ngogang J, Salah JN, Minkoulou E, Balkau B. Prevalence of NIDDM and impaired glucose tolerance in a rural and urban population in Cameroon. Diabetologia (1997); 40: 824± 829. 15. AhreÂn B and Corrigan CB. Prevalence of diabetes mellitus in north-western Tanzania. Diabetologia (1984); 26: 333± 336. 16. McLarty DG, Swai ABM, Kitange HM, Masuki G, Mtinangi BL, Kilima PM et al. Prevalence of diabetes and impaired glucose tolerance in rural Tanzania. Lancet (1989); 1: 871± 875. 17. Papoz L, Ben Khalifa F, EshweÁge E, Ben Ayed H. Diabetes mellitus in Tunisia: descriptions in urban and rural populations. Int J Epidemiol (1988); 17: 419±422. 18. Herman WH, Ali MA, Aubert RE, Engelgau MM, Kenny SJ, Gunter EW et al. Diabetes mellitus in Egypt: risk factors and prevalence. Diabetic Med (1995); 12: 1126± 1131. 19. Elbagir MN, Eltom MA, Elmahadi EMA, Kadam IMS, Berne C. A population-based study of the prevalence of diabetes and impaired glucose tolerance in adults in northern Sudan. Diabetes Care (1996); 19: 1126± 1128. 20. Elbagir MN, Eltom MA, Elmahadi EMA, Kadam IMS, Berne C. A high prevalence of diabetes mellitus and impaired glucose tolerance in the Danagla community in northern Sudan. Diabetic Med (1998); 15: 164± 169.

21. Omar MAK, Seedat MA, Motala AA, Dyer RB, Becker P. The prevalence of diabetes mellitus and impaired glucose tolerance in a group of urban South African Blacks. S Afr Med J (1993); 83: 641±643. 22. Levitt NS, Katzenellenbogen JM, Bradshaw D, Hoffman MN, Bonnici F. The prevalence and identification of risk factors for NIDDM in urban Africans in Cape Town, South Africa. Diabetes Care (1993); 16: 601± 607. 23. Mollentze WF, Moore AJ, Steyn AF, Joubert G, Steyn K, Oosthuizen GM, Weich DJV. Coronary heart disease risk factors in a rural and urban Orange Free State black population. S Afr Med J (1995); 85: 90 ± 96. 24. Dowse G, Zimmet P, King H. Relationship between prevalence of impaired glucose tolerance and NIDDM in a population. Diabetes Care (1991); 14: 968±974. 25. Ramaiya KL, Swai AB, McLarty DG, Alberti KG. Impaired glucose tolerance and diabetes mellitus in Hindu Indian immigrants in Dar es Salaam. Diabetic Med (1991); 8: 738± 744. 26. Omar MAK, Seedat MA, Dyer RB, Motala AA, Knight LT, Becker PJ. South African Indians show a high prevalence of NIDDM and bimodality in plasma glucose distribution patterns. Diabetes Care (1994); 17: 70 ± 73. 27. Alberti KGMM, Zimmet PZ, for the WHO Consultation. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO consultation. Diabetic Med (1998); 15: 539± 553. 28. The Expert Committee on the diagnosis and classification of diabetes mellitus. Report of the Expert Committee on the diagnosis and classification of diabetes mellitus. Diabetes Care (1997); 20: 1183±1197.

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South East Asia A. Ramachandran1, V. Mohan2, B.A.K. Khalid3 and A. Vichayanrat4 1

Diabetes Research Centre, Madras, India. 2 Madras Diabetes Research Foundation. 3 University Kebangsaan Malaysia, Kuala Lumpur, Malaysia. 4 Siriraj Hospital Medicine School, Bangkok, Thailand.

INTRODUCTION

EPIDEMIOLOGY OF TYPE 2 DIABETES

South East Asia geographically consists of Myanmar, Thailand, Cambodia, Laos, Vietnam, the Philippines, Indonesia, Malaysia, Singapore, Brunei, the nations that surround the South China Sea and the nations in the Indian subcontinent. The three major forms of diabetes described from South East Asia are Type 1 diabetes mellitus, Type 2 diabetes mellitus and malnutrition-related diabetes mellitus (MRDM). The prevalence of Type 1 diabetes is lower compared to several European countries. Type 2 diabetes constitutes over 90% of the diabetic population. MRDM, especially the fibrocalculous variety, is seen in some parts of India. In this chapter, we shall discuss the epidemiology of Type 1 and Type 2 diabetes in South East Asia, as MRDM is described in detail in chapter 18. The prevalence of Type 2 diabetes varies in different geographic regions and in different ethnic groups (1). The WHO Ad Hoc Diabetes Report published in 1993 showed that the age-standardized prevalence ratio of diabetes within a chosen age range was low (< 3%) or absent in certain traditional communities in developing countries, about 3±10% in European populations, and as high as 14±20% in migrant Asian Indians, Chinese and Hispanic American populations. In rural Papua New Guinea (Pacific region) Type 2 diabetes was virtually unknown (2). Epidemiological studies during the last two decades have shown that the prevalence of diabetes is steadily increasing in populations in South East Asia. In this chapter, we will examine the data accumulated so far to substantiate this.

A review of the literature available in 1987 showed the estimated prevalence of diabetes in the South East Asian region to be about 2 ±5% (3, 4). The earliest recorded studies in Malaysia, Singapore and Indonesia showed similar low estimates for Malays and Indonesians, about 1± 2% for Malays in Malaysia in the 1960s, 1.4% for Malays in Singapore in 1945 and 1.5% in Indonesians in 1976. These low estimates may, however, be methodological due to the small numbers of subjects tested and the use of urine testing. The first authentic data on the prevalence of diabetes in India came from the multicentric study conducted by the Indian Council of Medical Research (ICMR) in the early seventies. This study reported a prevalence of 2.3% in the urban and 1.5% in the rural areas. The criteria used in this study were different from those presently set by the WHO Expert Committee on Diabetes Mellitus. Indian migrants settled in different parts of the world had been shown to have high prevalence of Type 2 diabetes (6) which was believed to be due to greater affluence and a change to a more sedentary lifestyle as compared to the native Indian population (Table 14.1). However, the local host populations living in an identical environment in these countries still had only a low prevalence rate of diabetes. Assuming that Indians as an ethnic group have a high degree of genetic predisposition to develop diabetes, one could expect higher prevalence of diabetes among the native urban populations with a comparable affluent lifestyle.

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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Table 14.1 Prevalence of diabetes in migrant Indians and other ethnic groups Prevalence (%) Year

Author

Country

1958 1986 1967 1983

Wright et al. Beckles et al. Cassidy Zimmet et al.

Trinidad Trinidad Fiji Fiji

1969 1975 1988 1989 1989 1989 1992

Marine et al. Cheah et al. McKeigue et al. Simmons et al. Dowse et al. Swai et al. Cheah and Thai

South Africa Singapore East London Coventry, UK Mauritius Tanzania Singapore Malaysia

Europeans

Africans

10.2

1.4 14.8

3.6 4.0 2.8 1.9

Melanesians

7.1 (U)

0.6

1.2 (R)

Malays

6.6 2.4

Chinese

1.6 11.5

9.3 3.0

Creoles

10.3

8.0 4.9

Indians 1.7 21.6 5.7 11.0 (U) 10.4 6.1 23.0 11.2 12.5 7.1 12.8 16.0

11.3 (R)

U = urban; R = rural.

Rising prevalence of Type 2 diabetes has been noted in Indians since 1986. Studies by Verma et al. in 1986 (7), using a questionnaire method, reported a prevalence of 3.1% in an affluent area in Darya Ganji, New Delhi. Over the same period, Ramachandran et al., in a series of cross-sectional surveys in Southern India, noted a rising trend in the prevalence of Type 2 diabetes. In 1983 (8), the prevalence of Type 2 diabetes in adults was 5% in an urban township, and it rose to 8.2% in 1988 (9). In a more recent survey in the same urban area, the prevalence has increased to 12%, thus highlighting the rising trend in the prevalence of Type 2 diabetes in urban India (10). The prevalence is comparable to that in migrant Indians. Cheah and Thai reported an increasing prevalence of Type 2 diabetes in Singapore, the overall prevalence rising 1.99% in 1975 to 4.7% in 1984 with a further increase to 8.6% in 1992 (11). The rise in prevalence occurred in Chinese (4% in 1984 to 8% in 1992), Malays (7.6% in 1984 to 9.3% in 1992) and Indians; but the most predominant change was in the Indians who had a 44% rise, i.e. from 8.9% in 1984 to 12.8% in 1992. The changes were attributed to the rising affluence, change in lifestyle with increasing obesity, and the migration of rural people to urban areas. In Kuala Lumpur, migrant Indians had a prevalence of 4.2% in 1966, 6.1% in 1975 and 16% in 1988 (12). The first nationwide survey of diabetes mellitus in Thailand was conducted in 1971 by the Diabetic Association of Thailand. Of the 322 953 people

screened, the prevalence of diabetes was estimated to be 2.5% (13). There were several studies in communities later on which showed an increasing prevalence of diabetes (14). Surveys of government savings bank employees (age 30 ±60 yr) revealed that prevalence of diabetes increased from 2.5% in 1978 to 3.3% in 1983 and 4.6% in 1986. A study on 3495 employees (age 35 ±54 yr) of the Electric general authority of Thailand (EGAT) revealed a prevalence of 6.0% in 1986. These people represented the middle income class in Bangkok. The prevalence of diabetes in the lower socio-economic community was studied in 1990 (15). At Klong Toey port area in Bangkok, the prevalence was 4.5 and 5.9% among slum residents and apartment house residents aged 30 years or over respectively. URBAN-RURAL DIFFERENCES If environmental factors do have a significant role in unmasking diabetes, one would expect a lower prevalence in the rural areas where the populations follow a conventional lifestyle. Such an urban± rural difference in the prevalence rates was found in a survey conducted in several countries. In Madras, in Southern India, two populations belonging to different socio-economic status showed wide differences in the prevalence of diabetes (8.2% in the urban and 2.4% in the rural populations) (9).

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A study from Orissa state, India (16), and the multicentric study by the ICMR have also shown that the prevalence of diabetes is higher in urban areas compared to the rural areas (6). Only in Fiji did rural and urban Indians have similar prevalence of diabetes (13.1 vs. 12.9% for men, 11.3 vs. 11.0% for women) (17). All these studies clearly indicate that the prevalence of Type 2 diabetes is high among Indians in the urban areas. Recently, Rao et al. conducted a comparative study of native rural Indians and migrant Indians in Malaysia and Guyana aged æ24 years (18). The overall prevalence of diabetes in the three groups was 2.8, 13.41 and 13.84% respectively, agespecific prevalence between 45 and 64 years was 4.69% in rural India, 23.39% in Malaysia and 23.63% in Guyana. A study of Kodali and Alberti reports on the transrural migration of native Indians (19). Improved socio-economic status with association of risk factors such as age, changes in dietary pattern, obesity and sedentary activity produced a higher prevalence of diabetes in the migrants (9.1%) vs. the local population (2.2%). Zimmet's study among Indians in Fiji showed the absence of rural ± urban difference in Indians which further substantiates the genetic susceptibility. Migrant Asian Indians in Fiji had a very high prevalence rate of diabetes. This was similar in both urban and rural populations and was attributed to similar dietary habits and the sedentary lifestyles in these two populations (17). The prevalence of diabetes in the rural population in Thailand was not low compared with the urban as previously thought. A study on 13 rural villages of Phon district in Khon Khaen (about 500 km northeast of Bangkok), using the WHO criteria revealed a prevalence of 6.7% (20). Aging, increased body mass index (BMI) and decreased physical activity were associated with diabetes in this study. Similar findings were also made in Malaysia. Recently, a survey of the rural community in Malaysia was repeated to compare the prevalence to the original survey of 1984 which recorded a prevalence of 3.9%. This survey in 1994 showed a crude prevalence of 14.6% which, when adjusted for age, was 12.2%. Thus, there had been an increase of 21.2% over 10 years, greater than that seen for Singapore (21). The prevalence of diabetes in Vietnam is still relatively low, being 2.5% in Ho

Table 14.2 Prevalence of Type 2 diabetes in South East Asia

India

Singapore Chinese

Year

Urban (%)

1972 1979 1988 1992

2.3 3.0 5.0 8.2

1984 1992 Malays 1984 1992 Migrant 1984 Indians 1992 Philippines 1992 Malaysia 1988 1994 Thailand 1971 1986 1989 Vietnam Sri Lanka M W

4.0 8.0 7.6 9.3 8.9 12.8 8.4±12.0 6.6 2.5 6.0 2.5 5.1 2.4

Rural (%) 1.3 2.4

Reference no. 6 4 8 9 10 10 10

3.8±9.7 7.4 14.6 6.0 6.7 1.4

10

12 Mai Theh Trach 2

Chi Minh City, 1.4% in Hanoi, and 0.55% in rural areas of Vietnam. The prevalence in ethnic Vietnamese is 2.5% and in ethnic Chinese is 2.8% (personal communication, Professor Mai Theh Trach, Vietnam). There are no data on the prevalence amongst the other indigenous groups such as the Dayaks and Kadazans of Borneo. Table 14.2 summarizes the findings on the prevalence of Type 2 diabetes in South East Asia. PREVALENCE OF IGT A number of epidemiological studies both in Asia and among the migrant south Asians have consistently documented high prevalence of impaired glucose tolerance (IGT) among south Asians and Indians (Tanzania, Mauritius, Fiji, Madras) (2). Studies in Madras were significant in this respect because the urban-rural difference in Type 2 diabetes in Southern India was conspicuous by its absence in the prevalence of IGT (9). Similar uniform high prevalences of IGT have been reported among Fiji Indians. Previous studies have shown a high conversion rate of IGT to diabetes (35% in 5 years) in Southern India. Therefore high rates of IGT, especially in rural

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populations having low rates of diabetes, furnish evidence of a large potential diabetes pool for future (2). In Malaysia, Osman et al. (22) found prevalence of IGT to be inversely correlated to socio-economic status and to the prevalence of diabetes. In very rural villages, the prevalence of IGT was 15.0%, diabetes 2.7%; in a land resettlement scheme, IGT was 8.5%, diabetes 7.4%, and in urban Kuala Lumpur, IGT was 7%, diabetes 8.2%. In the aboriginal Orang Asli, however, with prevalence of diabetes 0.3%, IGT was only 4.7%, implying less risk of IGT and diabetes in this racial group (22). In their latest study in rural villages in 1994, the crude prevalence of diabetes was 14.6% and IGT 11.5%, implying higher risk of diabetes and IGT in rural Malays (21). This is in contrast to the finding of the urban survey in 1988 where prevalence of diabetes was 6.6% but IGT was only 1.9%. In Thailand, in the Khon Khaen study, 9.3% and 12.6% of men and women were found to have an abnormal oral glucose tolerance test. The figures from the Klong Toey study were 6.1 and 4.4% for the slum and apartment house residents respectively. A study on the patterns of glucose tolerance after 2 years of diagnosis of IGT revealed that 16.3% of the subjects had become diabetic and 4% reverted to normal when they were retested (23). RISK FACTORS FOR TYPE 2 DIABETES AMONG SOUTH ASIANS Due to its rapid industrialization and economic growth, South East Asia is facing a Westernization of lifestyles and food habits, and increased rural to urban migration. There are also better health facilities and thus earlier detection of diseases much as diabetes. FAMILIAL AGGREGATION IN TYPE 2 DIABETES There are a number of epidemiological factors to indicate a strong genetic component in the causation of Type 2 diabetes. Recent studies in Madras showed that parental diabetes was present in 54% of Type 2 diabetes patients, with no maternal or paternal excess (24). A growing risk of

diabetes with increasing familial aggregation has been shown by the development of diabetes in the offspring two decades earlier than their parents. Several communities in India have a high rate of consanguinity and inbreeding. This is probably also a factor contributing to the high prevalence of diabetes in the Tamil Indian community in South Africa (25). Prevalence of diabetes was found to be high in Parsees in north-western India and in tribal populations in Orissa, India, providing evidence for higher genetic susceptibility in inbred populations (25). OBESITY Recent studies have highlighted that in addition to general obesity, the regional distribution of body fat is significantly associated with diabetes and cardiovascular disease. Central adiposity as indicated by waist:hip ratio measurement has been associated with high risk of diabetes. Studies in Southern India showed that even in the non-obese population, this association holds true (9). McKeigue's studies in London, comparing south Asians and Europeans, showed that the Asians have high prevalence of diabetes as well as coronary heart disease and its risk factors. Interestingly, for a given BMI, the Asian subjects had a higher waist : hip ratio compared to the Europeans (26). Obesity is becoming more common in urban Thailand. Body mass index of 25± 29.9 (overweight) was reported in 23.3% of Thai men employed in the Electric General Authority aged 35 ±54 years (27). The corresponding figure in women was 18.4%. In rural Thais in the Khon Khaean study, 17.0% subjects were found to be overweight. Of interest was the much higher prevalence of obese (BMI > 30) subjects in the lower socio-economic classes in Klong Toey port areas being 10 and 11% in the slum and apartment house residents respectively. The prevalences of overweight and obese patients with Type 2 diabetes in Thailand were 32.1% and 14.2% respectively (14). In Kuala Lumpur, the prevalence of diabetes was 7.3% in obese and 1.6% in non-obese subjects (22). The community-based studies done in three different socio-economic locations in Malaysia clearly showed a positive correlation with economic status,

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educational attainment, family income and location (22). The prevalence was only 2.8% in rural villages, 6.7% in the higher socio-economic location of a land resettlement scheme, and 8.2% in urban Kuala Lumpur. The prevalence was also associated with light physical activity and obesity. In the 1985 Singapore survey, the mean BMI in diabetics was 25.8 compared to 22.7 in non-diabetics. In Malaysia, diabetics had BMI of 28.3 compared to nondiabetic 23.7. EPIDEMIOLOGY OF TYPE 1 DIABETES It is generally believed that Type 1 diabetes is less common in the tropical regions, especially in South East Asian countries. Genetic as well as environmental factors may be involved in this. Recent studies in the UK interestingly showed that the incidence of Type 1 diabetes in Asian children is not significantly low compared to European children (28). Accurate information on the prevalence and incidence of Type 1 diabetes is not presently available from most of the Asian countries. There have been attempts to set up Type 1 diabetes registries to collect data in many cities. A recent population-based study of prevalence of Type 1 diabetes in Madras, in Southern India, showed that the prevalence was 0.26=1000 in children in the age group of 0 ± 15 years. This suggested that Type 1 diabetes was not rare in urban India and is an even higher rate than that reported from many other Asian countries (29). Nevertheless, it is much lower than the prevalence reported from North Europe (2.1=1000) and 1.54=1000). Type 1 diabetes affected 0.08% of children aged 0 ±15 years in Thailand. The annual incidence has been reported to be 0.14 and 0.19 per 100 000 in 1984 and 1985 respectively (30). The peak age of onset was later than in the European children. More than 60% of the patients developed Type 1 diabetes at the age of 11 ±15 years. In Singapore, the prevalence of Type 1 diabetes in Chinese children of age range 6 ±16 years was estimated to be 0.15 per 1000 (Prof. A.C. Thai, personal communication) and in Ho Chi Minh City, Vietnam, prevalence was 0.31% (Prof. Mai Theh Trach). The incidence of Type 1 diabetes has been estimated recently in Karachi by Staines et al.

as 0.06=100 000 per year (31). More data are needed from the South East Asian countries on the incidence and prevalence of Type 1 diabetes.

CONCLUSION The available epidemiological data show that the prevalence of Type 2 diabetes is rapidly increasing in all ethnic groups and nationalities in South East Asia. With the present rate of high economic growth in this region a further increase in the prevalence of Type 2 diabetes is expected in the near future.

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Impaired glucose tolerance in Ampur Phon, Khon Kaen. National Epidemiology Board of Thailand, 1989. Khebir BV, Osman A; Khalid BAK. Diabetes mellitus among rural Malays in Kuala Selangor: risk factors and trend. Med J Malaysia (1996); 51. Osman A, Khalid BAK, Tan TT, Wu LL, Sakinah O, Ng ML. Prevalence of NIDDM and impaired glucose tolerance in aborigines and Malays in Malaysia and their relationship to socio-demographic, health and nutritional factors. Diabetes Care (1993); 16: 68±75. Puavilai G, Tiewtranon V, Pensuwan S et al. Impaired glucose tolerance after 2-year follow-up. J Med Ass Thailand (1987); 70 (suppl 2): 68± 76. Viswanathan M, McCarthy MI, Snehalatha C, Hitman GA, Ramachandran A. Familial aggregation of Type 2 (non-insulin dependent) diabetes mellitus in south India: absence of excess maternal transmission. Diabetic Med (1996); 13: 232± 7. Zimmet PZ. Challenges in diabetes epidemiology: from West to the rest. Diabetes Care (1992); 15: 232±252. McKeigue PM, Shah B, Marmot MG. Relation of central obesity and insulin resistance with high diabetes prevalence and cardiovascular risk in south Asians. Lancet (1991); 337: 382± 386. Tanphaichitr V, Kulapongse S, Pakpeankitvatana R, Leelahagul P, Tanwiwat C, Lochaya S. Prevalence of obesity and its associated risks in urban Thais. In: Y Oomura, S Tami, S Inoue, T Shimazu (eds), Progress in Obesity Research 1990. London, John Libbey, 1991: pp. 649± 653. Bodansky HJ, Staines A, Stephenson C, Haigh D, Cartwright R. Evidence for an environmental effect in the aetiology of insulin dependent diabetes in a transmigratory population. Fr Med J (1992); 304: 1020±1022. Ramachandran A, Snehalatha C, Abdul Khader OMS, Annie Joseph T, Viswanathan M. Prevalence of childhood diabetes in an urban population in south India. Diab Res Clin Pract (1992); 17: 227±231. Tuchinda C, Angusingha K, Chaichanwatanatul K, Likitmaskul S, Vannasaeng S. The epidemiology of insulin-dependent diabetes mellitus (NIDDM): report from Thailand. J Med Ass Thailand (1992); 7: 217±222. Staines A, Ahmed S, Hanif S, Bodansky HJ. The incidence of childhood insulin-dependent diabetes mellitus in Karachi, Pakistan (Abstract), p. 20.

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15

Pacific Island Populations 1

Daniel J. McCarty,1 Paul Zimmet2

University of Melbourne, Australia 2International Diabetes Institute, Melbourne, Australia

Recently published global estimates of diabetes suggest that around 110 ± 135 million people, or about 2± 2.5% of the total population, have Type 2 diabetes (1 ± 4). Furthermore, these reports project that the number of people with Type 2 diabetes worldwide may double within the next 10 ±25 years, with the greatest increases expected in developing or newly industrialized countries (1, 3± 4). As with the pathogenesis of Type 2 diabetes, the environmental and genetic factors underpinning this global epidemic on the population level are complex. Part of the epidemic will likely be due to the `demographic transition': increasing longevity, shifts in population age structures towards older age groups and rising urbanization. The `epidemiological transition' (5), or changes in disease morbidity and mortality patterns from communicable to non-communicable disease associated with improved nutrition, hygiene and access to health care, will also be a contributing factor. Finally, continued global economic development and modernization will mean that more people will be adopting Westernized lifestyles which, along with many benefits, also include the characteristics of high-fat, energy-dense diets and decreased levels of physical activity, important risk factors for the development of obesity and Type 2 diabetes. The corresponding burden of complications and premature mortality resulting from diabetes will constitute a serious and growing public health problem for most countries. Arguably, the studies which have most clearly shown the association of lifestyle change with obesity and diabetes have been conducted in Pacific Island populations over the past 25 years (7 ± 24). Most of the Pacific Island populations have undergone dramatic demographic and epidemiologic changes in recent decades. In this chapter

we review available epidemiological prevalence and diabetes risk factor data for Pacific Island populations. TYPE 2 DIABETES PREVALENCE IN THE PACIFIC The prevalence of Type 2 diabetes varies tremendously in Pacific Island populations, from none to very few cases in Papua New Guinea (PNG) highlanders (11) to between 40 and 45% in the urbanized Koki of PNG (13) and in Nauruans (16) (Figure 15.1). Some of the highest rates ever reported were found in Melanesian, Micronesian and Polynesian populations. Some of this variation in diabetes rates reflects minor differences in the age range of the subjects studied, sample sizes and the criteria used to classify diabetes (25 ± 27). It appears that much of the variation in Type 2 diabetes prevalence relates to the degree of development (or Westernization) of the population studied, as evidenced by consistently higher rates in urban than rural areas and the exceptionally high rates in the most developed countries (6). Type 2 diabetes is also a significant problem in Australian Aborigine and Torres Strait Islander communities (7, 8, 28). Although the heterogeneity of these populations prohibit the calculation of a summary prevalence estimate, available data suggest that Type 2 diabetes prevalence is between 10 and 30% in adults (28). Diabetes prevalence appears to have increased dramatically in Pacific Island countries, as evidenced by repeated cross-sectional surveys conducted in PNG (11±13) and Western Samoa (23±24). However in Nauru, where the population has experienced many years of Westernization, data suggest that the

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

females in these regions may explain the variation in the male : female ratio of diabetes cases in these studies. Figure 15.1 shows the age-standardized prevalence of Type 2 diabetes in the Pacific region for people aged 35 ± 64 years. The individual rates were standardized to Segi's World Population (29) to allow direct comparison between the countries. Some of these studies were conducted a number of years ago. Therefore it is likely that the data presented are underestimates of the current rates of diabetes in those countries that have experienced modernization with concomitant increases in lifestyle-related non-communicable diseases. Impaired Glucose Tolerance (IGT)

Figure 15.1 Type 2 diabetes prevalence in Pacific Island populations. All values are age-standardized to Segi's world population (29) for the age group 35 ±64 years

prevalence of diabetes may have reached a plateau (16±18). Observations within countries suggest large differences in prevalence between urban and rural areas. Without exception, urban rates of diabetes exceed those of the corresponding rural areas, supporting the notion that increasing urbanization is associated with an increasing prevalence of lifestyle-related diseases such as diabetes. The studies in PNG (11 ± 13) illustrate quite clearly the increasing diabetes prevalence over time, the urban=rural variation in rates and differing genetic susceptibility among subgroups of the population. However, the two rural PNG populations most recently surveyed have a 6-fold difference in diabetes prevalence (2% vs 12.4%), suggesting that diabetes prevalence and factors determining the patterns of diabetes in these countries are very complex (13). Comparison of diabetes rates between males and females in the Pacific Islands generally shows little variation (data not shown). Exceptions to this include Ouvea (10) and Tuvalu (20) where the majority of newly diagnosed subjects with diabetes were women. In contrast, in rural PNG (12) all cases identified were men. Differences in levels of physical activity and of obesity between males and

The prevalence of impaired glucose tolerance (IGT) is thought to be a useful indicator of diabetes potential as a person with IGT is at increased risk of developing diabetes in comparison to those with normal glucose tolerance (30). In many Pacific Island and migrant Asian populations IGT levels are often lower than the diabetes rates, particularly in urban areas. Therefore, one could expect that the prevalence of diabetes is closer to its maximum level in these populations (30). Undiagnosed Diabetes Evidence suggests that the majority of Pacific Islanders with diabetes are likely to be undiagnosed, which indicates a serious potential for severe complications and premature mortality resulting from extended periods of poor glycaemic control. The prevalence of undiagnosed diabetes in Pacific Island populations is generally quite high with 80 ±100% of classified cases not having a previous diabetes diagnosis in some communities (Table 15.1). ENVIRONMENTAL RISK FACTORS FOR TYPE 2 DIABETES Genetic susceptibility to Type 2 diabetes is clearly important, but there is also strong evidence that the disease is unmasked by environmental factors

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PACIFIC ISLAND POPULATIONS Table 15.1 Prevalence of IGT and diabetes in Pacific Island populations Ethnic group=country

Aboriginal Australian * Bourke Central Australia Indian Fiji Rural Urban Melanesian Fiji Rural Urban New Caledonia Touhu Ouvea Papua New Guinea NAN Ð Highlands NAN Ð Highlands AN Ð Rural AN Ð Periurban AN Kalo Ð Rural Wanigela Ð Rural Koki Ð Urban Vanuatu Mid. Bush Tanna Ð Rural Nguna Ð Semi-Rural Vila civil Servants Ð Urban Micronesian Kiribati Rural Urban Nauru

Part Polynesian New Caledonia Ouvea Polynesian New Caledonia Noumea Ð Wallis Isl. migrants Tuvalu Funafuti Wallis Island Rural Western Samoa Rural Urban Poutasi Ð Rural Tuasivi Ð Rural Apia Ð Urban Cook Islands Rarotonga Niue

Investigator= Year Published

Cameron et al. 1986 (7) O'Dea et al. 1993 (8) Zimmet et al. 1983 (9)

Zimmet et al. 1983 (9) Zimmet et al. 1982 (10)

King et al. 1984 (11) King et al. 1989 (12)

Dowse et al. 1994 (13)

Taylor et al. 1991 (14)

King et al. 1984 (15)

Sample size (n)

294 245 108 846 452 477 863 172 535 308 257 269 273 541 197 664 397 544 632

Age Diabetes IGT Newly Previously Total crude range classification * (%) diagnosed diagnosed prevalence (years) (%) (%) diabetes (%) æ20 Æ35 >35

a c

æ20

b

æ20

b

æ20

a

æ20 æ20

b c

æ25

c

æ20

a

2.4 4.7 14.8

39.1 ± ±

60.1 ± ±

54.4

45.6

8.2 11.1

62.5 64.9

37.5 35.1

1.7 6.6

5.8 5.0

66.7 91.7

33.3 8.3

1.7 2.2

2.3

±

±

0.0

1.9 0.0 0.7

± 100.0 72.7

± 0.0 27.3

0.0 0.7 4.0

4.1 18.5 21.1

± 26.9 63.0

± 73.1 37.0

2.0 12.4 30.1

2.4 2.3 2.3

± ± ±

± ± ±

1.0 1.5 5.1

83.8 80.0 69.7 40.9 26.9 ±

16.2 20.0 30.3 50.1 73.1 ±

3.6 7.7 34.4 24.2 27.9 28.1

10.4 10.6

15.6 5.3 29.6 13.1 12.8

æ20

b

Zimmet et al. 1977 (16) Zimmet et al. 1984 (17) Dowse et al. 1991 (18) Dowse et al. 1994 (13)

1031 1880 221 1583 1213 1404

æ20 æ20 æ20 25 ±74

d b c c

13.5 16.0 11.3 18.4 11.4 16.2

Zimmet et al. 1982 (10)

401

æ20

a

8.0

85.7

14.3

7.0

Taylor et al. 1985 (19)

564

20 ±64

b

8.7

±

±

11.9

Zimmet et al. 1977 (20)

397

æ20

d

13.6

70.6

29.4

4.3

Taylor et al. 1983 (21) Zimmet et al. 1981 (22)

549

æ20 æ20

b e

5.5

73.3

26.7

2.6

æ20

c

3.9 8.3

44.0 60.0

56.0 40.0

3.4 8.7

æ20

c

8.2 5.3 9.6

60.0 46.8 49.2

40.0 53.2 50.8

6.5 9.0 16.0

9.6 6.9

50.0 49.4

50.0 50.6

6.7 7.4

Collins et al. 1994 (23)

King et al. 1986 (24)

745 744 463 524 785 1102 1128

AN = Austronesian (coastal) ancestry, NAN = non-Austronesian (highland) ancestry. *Selected studies: a WHO, 1980 (25); b WHO 1980 and NDDG 1979 (27); e WHO, 1986 (28); d 75 g OGTT; DM = 2 h-PG æ 160 mg=ml; e NDDG.

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(31, 32). The most recognised environmental determinants of Type 2 diabetes (i.e. changing diet, physical inactivity and obesity), can be assessed as targets for intervention and prevention of this disease. Changing Diet Although no specific nutrients have been identified which directly affect insulin resistance, dietary intake is recognized as an important risk factor for Type 2 diabetes through its role in obesity. Increased intake of refined carbohydrate and saturated fats and decreased intake of dietary fibre have been demonstrated to decrease insulin sensitivity and lead to abnormal glucose tolerance by promoting obesity (33, 34). Alleviation of diseases associated with poverty and undernutrition has been particularly evident in Asia=Pacific countries in recent years, but the scales may have tipped too far with an overabundance of unhealthy foods (35). This phenomenon is common to the developing countries of the Asia=Pacific region. Epidemic Obesity An epidemic of obesity in the Pacific has contributed to the rise of non-communicable diseases, Type 2 diabetes in particular (36). The patterns of obesity mirror those of Type 2 diabetes prevalence, with a larger proportion of obesity in urban than rural areas and an even greater prevalence in migrant populations in more Westernized countries. The body mass index (BMI) is widely used as a reference measure for body mass with values forming a continuum from underweight to obese. The BMI criterion used to classify obesity differs between studies and ethnic groups and it is argued that a lower threshold for obesity should be applied in Asian populations. The prevalence of overall obesity (BMI >30 kg=m2) in the Pacific Islands ranges from a low of 3.3% (men) and 2.2% (women) in the rural PNG highlands to 77.3% (men) and 77.1% (women) in Nauru (36). In general, women tend to have a higher prevalence of obesity than men, with often more than a 2-fold difference. This pattern is not mirrored in diabetes prevalence, with similar rates for males and females.

In Asian and Pacific populations, it is thought that central (abdominal) fat distribution, measured with the waist ± hip ratio, is an important risk factor for diabetes which acts independently of overall obesity (37 ±41). Obesity in Polynesian and Micronesian societies has been valued as a symbol of status and prosperity for many centuries (42 ± 43). The cultural value placed on obesity may provide a challenging barrier to intervention with weight loss as the target. Food culture, beliefs and knowledge must be taken into account when planning any lifestyle intervention to decrease the prevalence of diseases such as diabetes. Decreasing Physical Activity Physical activity has been reported to increase insulin sensitivity and improve glucose tolerance (44). The protective effect of exercise was illustrated in a study in the USA whereby a decreased incidence of diabetes was observed following an increase in physical activity levels (45). Lifestyle changes associated with urbanization in Asia and the Pacific have led to a reduction in physical activity, due in part to a reduced dependence on intensive manual labour to maintain subsistence. Several studies in Asian and Pacific Island populations have supported the notion that a significant association exists between the level of physical activity and glucose tolerance (18, 46, 47). When comparing urban and rural populations, people residing in rural areas have higher levels of physical activity than their urban counterparts and a correspondingly lower prevalence of diabetes (48). The association between physical activity and diabetes remains even after adjusting for obesity, hypertension and family history of Type 2 diabetes (45). Increasing physical activity should be an important component of strategies aimed at the prevention of diabetes and improvement of insulin sensitivity in affected individuals (49). THRIFTY GENOTYPE, THRIFTY PHENOTYPE OR BOTH? The high prevalence of diabetes and obesity in many developing nations has been described as

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PACIFIC ISLAND POPULATIONS

`arising from a collision of our hunter-gatherer genes with our new twentieth-century lifestyle' (50). In 1961, Neel proposed a `thrifty genotype' to explain this phenomenon (51). He suggested that optimal energy storage during times of plenty offered a survival advantage in times of nutritional hardship. With modernization, people have greater access to high-energy, low-fibre refined food and generally decrease their traditional activity levels. The survival mechanism for times of nutritional hardship may act as a disadvantage in times of nutritional plenty through the promotion of selective tissue insulin resistance and high basal and stimulated insulin concentrations. This could then eventually lead to obesity and lifestylerelated non-communicable diseases (51). Lifestyle changes and increasing longevity combined with thrifty genes may form the foundation of the epidemic of Type 2 diabetes which is currently occurring in the Pacific region.

4.

5. 6.

7.

8.

9.

CONCLUSION The magnitude of the diabetes epidemic in the Pacific region coupled with the significant morbidity and mortality associated with diabetic complications heralds the need for increased attention and resources to primary prevention of diabetes. The fact that the strongest environmental risk factors are potentially modifiable, points to lifestyle intervention, with the incorporation of a healthy diet and increased physical activity, as a means of curbing the impact of this epidemic in the Pacific region. Promotion of healthy lifestyles, while respecting local culture, poses an enormous challenge but is essential to optimize health for all Pacific Islanders.

10.

11. 12.

13.

14.

REFERENCES 1. McCarty D, Zimmet P. Diabetes 1994 to 2010: Global Estimates and Projections. Melbourne, Australia International Diabetes Institute, 1994. 2. Murray CJL, Lopez AD. Global Health Statistics: Global Burden of Disease and Injury, Series. Vol. II. Boston, MA, Harvard School of Public Health, 1996: pp. 586±600. 3. Amos A, McCarty DJ, Zimmet P. The rising global burden of diabetes and its complications: estimates

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and projections to the year 2010. Diabetic Med (1997); 14 (suppl 5): S1± S85. King H, Aubert RE, Herman WH. Global burden of diabetes, 1995± 2025: prevalence, numerical estimates, and projections. Diabetes Care (1998); 21: 1414± 1431. Omran A. The epidemiologic transition: a theory of the epidemiology of population change. Milbank Quarterly (1971); 49(4): 509± 538. Coughlan A, McCarty DJ, Jorgensen LN, Zimmet P. The epidemic of NIDDM in Asian and Pacific Island populations: prevalence and risk factors. Horm Metab Res (1997); 29: 323± 331. Cameron WI, Moffitt PS, Williams DRR. Diabetes mellitus in the Australian Aborigines of Bourke, New South Wales. Diabetes Res Clin. Pract. (1986); 2: 307± 314. O'Dea K, Patel M, Kubisch D, Hopper J, Traianedes K. Obesity, diabetes, and hyperlipidemia in a central Australian Aboriginal community with a long history of acculturation. Diabetes Care (1993); 16(7): 1004± 1010. Zimmet P, Taylor R, Ram P et al. Prevalence of diabetes mellitus and impaired glucose tolerance in biracial (Melanesian and Indian) population of Fiji: a rural-urban comparison. Am J Epidemiol (1983); 118(5): 673± 688. Zimmet P, Canteloube D, Genella B et al. The prevalence of diabetes mellitus and impaired glucose tolerance in Melanesians and Part-Polynesians in Rural New Caledonia and Ouvea (Loyalty Islands). Diabetologia (1982); 23: 393± 398. King H, Heywood P, Zimmet P et al. Glucose tolerance in a highland population in Papua New Guinea. Diabetes Res (1984); 1: 45 ± 51. King H, Finch C, Collins A et al. Glucose tolerance in Papua New Guinea: ethnic differences, association with environmental and behavioural factors and the possible emergence of glucose intolerance in a highland community. Med J Aust (1989); 151: 204±210. Dowse GK, Spark RA, Mavo B et al. Extraordinary prevalence of non-insulin-dependent diabetes mellitus and bimodal plasma glucose distribution in the Wanigela people of Papua New Guinea. Med J Aust (1994); 160: 767± 774. Taylor R, Jalaudin B, Levy S, Montaville B, Gee K, Sladden T. Prevalence of diabetes, hypertension and obesity at different levels of urbanisation in Vanuatu. Med J Aust (1991); 155: 86 ± 90. King H, Taylor R, Zimmet P et al. Non-insulindependent diabetes (Type 2 diabetes) in a newly independent Pacific nation: The Republic of Kiribati. Diabetes Care (1984); 7(5): 409± 415. Zimmet P, Taft P, Guinea A, Guthrie W, Thoma K. The high prevalence of diabetes mellitus on a central Pacific Island. Diabetologia (1977); 13: 111±115. Zimmet P, King H, Taylor R et al. The high prevalence of diabetes mellitus, impaired glucose

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tolerance and diabetic retinopathy in NauruÐ The 1982 survey. Diabetes Res (1984); 1: 13 ± 18. Dowse GK, Zimmet PZ, Finch CF, Collins VR. Decline in incidence of epidemic glucose intolerance in Nauruans: implications for the `Thrifty Genotype'. Am J Epidemiol (1991); 133(11): 1093±1104. Taylor R, Bennett P, Uili R et al. Diabetes in Wallis Polynesians: a comparison of residents of Wallis Island and first generation migrants to Noumea, New Caledonia. Diabetes Res Clin Pract (1985); 1: 169±178. Zimmet P, Seluka A, Collins J, Currie P, Wicking J, DeBoer W. Diabetes mellitus in an urbanized, isolated Polynesian population. Diabetes (1977); 26(12): 1101± 1108. Taylor RJ, Bennett JPH, LeGonidec G et al. The prevalence of diabetes mellitus in a traditional-living Polynesian population: The Wallis Island survey. Diabetes Care (1083); 6(4): 334± 340. Zimmet P, Faaiuso S, Ainuu J, Whitehouse S, Milne B, DeBoer W. The prevalence of diabetes in the rural and urban Polynesian populations of Western Samoa. Diabetes 1981; 30(1): 45 ± 51. Collins VR, Dowse GK, Toelupe PM et al. Increasing prevalence of Type 2 diabetes in the Pacific Island population of Western Samoa over a 13-year period. Diabetes Care (1994) 17(4): 288± 296. King H, Taylor R, Koteka G et al. Glucose tolerance in Polynesia: population-based surveys in Rarotonga and Niue. Med J Aust (1986); 145: 505±510. WHO Diabetes Mellitus: Report of a WHO Study Group. Geneva, World Health Organization, 1985. WHO Expert Committee on Diabetes Mellitus. Second Report. Geneva, World Health Organization. 1980. National Diabetes Data Group. Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes (1979); 28: 1039± 1057. DeCourten M, Hodge A, Dowse G, King I, Vickery J, Zimmet P. Review of the epidemiology, aetiology, pathogenesis and preventability of diabetes in Aboriginal and Torres Strait Islander populations. Office for Aboriginal and Torres Strait Islander Health Services, Commonwealth of Australia, 1998. Segi M. Cancer mortality for selected sites in 24 countries (1950± 57). Tohuku University School of Medicine, Sendai, 1960. Dowse GK, Zimmet PZ, King H. Relationship between prevalence of impaired glucose tolerance and Type 2 diabetes in a population. Diabetes Care (1991); 14(11): 968± 974. Zimmet P. Challenges in diabetes epidemiology Ð from West to the rest. Diabetes Care (1992); 15: 232±252. Bennett P, Bogardus C, Tuomilehto J, Zimmet P. Epidemiology and natural history of Type 2 diabetes: Non-obese and obese. In: K Alberti, R

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DeFronzo, H Keen, P Zimmet (eds), International Textbook of Diabetes Mellitus, Chichester, Wiley, (1992): pp. 147± 176. Hamman R. Genetic and environmental determinants of non-insulin-dependent diabetes mellitus (Type 2 diabetes). Diabetes Metab Rev (1992); 8(4): 287±338. Hodge AM, Dowse GK, Zimmet. Diet does not predict incidence or prevalence of non-insulindependent diabetes in Nauruans. Asia Pacific J Clin Nutr (1993); 2: 35 ±41. Chen A, Ge K. Nutrition transition in China: the growth of affluent diseases with the alleviation of undernutrition. Asia Pacific J Clin Nutr (1995); 4: 287±293. Hodge AM, Dowse GK, Zimmet PZ. Obesity in Pacific populations. Pacific Health Dialog (1996); 3(1): 77 ±86. Ohlson L, Larsson B, Svardsudd K et al. The influence of body fat distribution on the incidence of diabetes mellitus. Diabetes (1985); 34: 1055 ±1058. Zimmet PZ, King HOM, BjoÈrntorp SPA. Obesity, hypertension, carbohydrate disorders and the risk of chronic diseases. Med J Aust (1986); 145: 256± 262. Anderson P, Chan J, Chan Y et al. Visceral fat and cardiovascular risk factors in Chinese Type 2 diabetes patients (Abstract). Diabetes towards the new millennium. The Third International Diabetes Federation Western Pacific Regional Congress, Hong Kong, 1996. Dowse GK, Zimmet PZ, Gareeboo H. Abdominal obesity and physical inactivity as risk factors for Type 2 diabetes and impaired glucose tolerance in Indian, Creole, and Chinese Mauritians. Diabetes Care (1991); 14(4): 271± 282. McKeigue PM, Pierpoint T, Ferrie JE, Marmot MG. Relationship of glucose intolerance and hyperinsulinaemia to body fat pattern in South Asians and Europeans. Diabetologia (1992); 35: 785±791. Langdon, R. The Lost Caravel. Sydney, Pacific Publications, 1975. Fabricius W. Nauru 1888± 1900. An account in German and English based on official records of the colonial section of the German Foreign Office held by the Deutches Zentralarchiv in Potsdam, ed. D. Clark and S. Firth. Canberra, Australian National University Press, 1992. BjoÈrntorp P. Effects of physical training on diabetes mellitus, Type II. In: H Bostrom, N Ljungstedt, (eds), Recent Trends in Diabetes Research. Stockholm, Almquist and Wiksell International, 1982. Helmrich SP, Ragland DR, Leung RW, Paffenbarger RS. Physical activity and reduced occurrence of non-insulin-dependent diabetes mellitus. N Engl J Med (1991); 325(3): 147± 152. Taylor P, Ram P, Zimmet P, Raper, Ringrose H. Physical activity and prevalence of diabetes in

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Melanesian and Indian men in Fiji. Diabetologia (1984); 27: 578± 582. 47. Zimmet PZ, Collins VR, Dowse GK et al. The relation of physical activity to cardiovascular disease risk factors in Mauritians. Am J Epidemiol (1991); 134(8): 862± 875. 48. Dowse GK, Hodge AM, Zimmet PZ. Paradise lost: obesity and diabetes in Pacific and Indian Ocean populations. In: A Angel et al. (eds), Progress in

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Obesity Research: 94. London, Libbey, 1996: pp. 227± 238. 49. WHO Prevention of diabetes mellitus: Report of a WHO Study Group (WHO Tech. Rep. Series No 844). Geneva, World Health Organization, 1994. 50. Diamond, J. Sweet death. Nat Hist (1992); 2: 2 ± 6. 51. Neel J. Diabetes mellitus: a thrifty genotype rendered detrimental by `progress'? Am J Hum Genet (1962); 14: 353± 362.

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16

China 1

Peter H. Bennett, Gungwei Li and Pan Xiaoren

National Institutes of Health, Phoenix, Arizona, USA, 2 China± Japan Friendship Hospital, Beijing, China

INTRODUCTION The prevalence of diabetes in China has generally been regarded to be low by most observers. Nevertheless, it is estimated that there are presently some 18.6 million persons with diabetes, a number greater than in any other country except India (1). China currently has a population of some 860 million aged 20 years and over and this number will increase to about 1200 million by AD 2025. TYPE 1 DIABETES Until recently, information about the occurrence of type 1 diabetes in China was sparse. As part of the WHO DIAMOND Project a registry for Type 1 diabetes was established in 22 centers to determine the incidence of the disease (2). New cases of diabetes, diagnosed and placed on insulin treatment before their 15th birthday, residing in defined registration areas, were identified. New cases were ascertained among 20.7 million children from many different geographical areas over a period ranging from 1985 to 1994. The overall annual incidence was estimated to be 0.51=100 000, an extremely low rate (2). There was, however, considerable geographic variation, with the higher rates in the North and Northeast and lower ones in the South of the country (Table 16.1). The majority (95%) of the population monitored were Han Chinese, but six other minority populations were represented in the study. The majority of these had rates higher than those seen in the Han population. Whenever unusually low rates of Type 1 diabetes are found there is always concern about the

completeness of ascertainment of cases. In this study a two-stage capture-recapture method was employed from which it was estimated that 94% of the diagnosed cases had been identified. The incidence rates reported are corrected for underascertainment. The study authors also believe that few cases were missed because of failure to diagnose the disease. The incidence rate of Type 1 diabetes in the mainland of China is only one-quarter that reported among Chinese in Singapore and Hong Kong, which also have a low rate by Western standards. Even though the genetic susceptibility of Chinese children to develop the disease may be low, the lower incidence in mainland Chinese children suggests that environmental factors play a major role in determining if the disease develops. The precipitating or protective factors remain to be elucidated. On the basis of this multicenter study the authors estimate that in China approximately 9750 children aged 10± 14 years develop the disease each year. TYPE 2 DIABETES Earlier studies of the prevalence of Type 2 diabetes were conducted in Shanghai in 1978, and in Beijing between 1979 and 1981 (3, 4). An overall prevalence of 1.01% was found in Shanghai and in Beijing 0.75%. Subsequently a nationwide survey of 314 895 subjects from 14 provinces and cities was performed (5). Among those under 40 years of age screening for glycosuria was performed, and in subjects aged 40 years and over, venous plasma glucose concentrations were determined before and after breakfast or lunch. Those with glycosuria and

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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THE EPIDEMIOLOGY OF DIABETES MELLITUS Table 16.1 Ascertainment-corrected annual incidence rates of Type 1 diabetes in six regions of China Region

Centers

Population

Observed cases

Estimated cases

Estimated rate= 100 000 (95% CI)

7 4 2 2 3 4

4 894 818 4 129 795 862 761 2 640 947 5 031 456 3 075 203

177 196 20 77 67 55

180 218 20 90 70 60

0.55(0.53±0.56) 0.79(0.73±0.85) 0.41(0.39±0.43) 0.68(0.57±0.80) 0.23(0.21±0.26) 0.36(0.31±0.41)

Northeast North middle Northwest Southeast South middle Southwest

Source: Adapted from reference (2).

those with plasma glucose values of >7.77 mmol=l post-prandially received a 100 g OGTT, unless their fasting plasma value was >7.22 (130 mg=dl) or the post-prandial was æ11.1 mmol=l (100 mg=dl) in which case they were assumed to have diabetes. Overall, the prevalence of diabetes was 0.67%. However, the design of the study and the criteria employed for diagnosis make comparison with other populations difficult. Nevertheless, rates of diabetes were similar in men and women, increased with age, and were higher among the overweight. There were appreciable differences among the provinces with rates being higher in Beijing and in the southern coastal provinces, than elsewhere. More recently, studies using the 1985 WHO criteria for the diagnosis of diabetes and impaired glucose tolerance have been carried out. DaQing To determine the prevalence of Type 2 diabetes and impaired glucose tolerance (IGT) and incidence of Type 2 diabetes among the Chinese, and to determine the effect of intervention therapy on IGT, a 10-year prospective study (1986 ±1996) was initiated in June 1986 in DaQing City (6). DaQing City is a new industrial city with a population of 500 000 in Heilongjiang Province in the north of China. This city is a petroleum production base. People in DaQing were gathered from all parts of China after 1960. There were 281 589 people with an age range of 25± 74 years in DaQing in 1986. All of them receive health care in assigned local clinics throughout the city. In the 1986 prevalence survey half of these clinics were randomly chosen. They provided medical care to

126 715 people in the age range of 25± 74 years. There were 110 660 subjects (87.3%) in the selected population; 55 391 men and 55 269 women who took part in the study. Of these, 190 had previously known diabetes, and the remainder were screened by measuring the 2 hour postbreakfast plasma glucose concentrations after taking about 100 g of steamed bread (equal to 80 g of carbohydrate); 4209 subjects had a 2 hour post-breakfast plasma glucose >6.67 mmol=1 (120 mg=dl), and 3,956 of them (94% of 4,209) received a 75 g OGTT. The prevalence of diabetes by WHO (1985) criteria in 1986 was similar in men and women. When standardized to the Chinese population (1982), the prevalence of diabetes was 1.25% (CI = 1.2 ± 1.33) and of IGT 0.77% (95% CI 0.71 ± 0.82%). NATIONAL PREVALENCE SURVEY, 1994 In 1994 the methods used in DaQing were then extended to survey populations in 19 of the 32 provinces and areas that comprise mainland China Table 16.2 (7). Capillary finger blood glucose concentrations were measured 2 hours after a breakfast containing at least 80 g carbohydrate. A total of 223 251 subjects aged 25 and over participated in the study. (89% of the study population) of whom 213 515 were aged 25± 64 years and of these 21 851 had a 2 hour capillary blood glucose value æ6.67 mmol=l (120 mg=dl). Those who were already taking insulin or oral hypoglycemic agents and those whose postbreakfast capillary blood glucose level was æ11.1 mmol=l (200 mg=dl) were taken to have

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CHINA Table 16.2 The prevalence (%) of diabetes (previously and newly diagnosed) in 1994 National Survey Age

Men

Women

With diabetes Sample N 25±34 35±44 45±54 55±64 Total

36 33 24 18 113

446 610 932 014 002

Standardized rate (95% CI)

With diabetes

Known N

New N

Prev

Sample N

15 114 257 417 803

89 392 639 771 1891

0.29 1.51 3.59 6.59 2.38

36 34 19 10 100

Men 2.21 (2.12±2.30)

114 404 243 752 513

Women 2.40 (2.31±2.49)

Known N

New N

Prev

12 95 248 285 640

102 361 496 571 1530

0.32 1.33 3.87 7.96 2.16

TOTAL 2.28 (2.22±2.34)

Adapted from reference (7).

diabetes. Those not currently taking either insulin or oral hypoglycemic agents and whose 2 hour glucose level was 1 kg), and the least with weight gain (gained > 1 kg). Subjects who gained weight to reach a specific

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TYPE 2 DIABETES AND OBESITY Type 2 diabetes % prevalence

Figure 19.2 1992 prevalence of non-insulin-dependent diabetes mellitus (both newly diagnosed and known) (Type 2 diabetes) by body mass index (BMI) in 1992 and weight change between 1987 and 1992 in the multiethnic population of Mauritius (n = 3647)

BMI level would not have been at that level for as long as those who had remained stable at that BMI, so would have a lower risk of Type 2 diabetes. Subjects who had lost weight to reach a specific BMI would have had some duration of an even greater degree of obesity which would contribute to the higher prevalence of Type 2 diabetes in this group compared with the weight maintainers or gainers at the same BMI level. In one of the few reports to actually examine the levels of glucose tolerance associated with different duration of self-reported obesity (based on percentage of standard weight ranging from 14 to 137% overweight), Ogilvie (29) observed that it took 5 ±18 years of obesity for glucose intolerance to develop, and 12± 38 years for diabetes to occur. In contrast to other studies, the degree of obesity was not associated with glucose tolerance. Evidence for a specific effect of weight gain on Type 2 diabetes comes from two American studies, where self-reported weight gain throughout adulthood or immediately prior to the study period was associated with increased risk of Type 2 diabetes independent of BMI in early adulthood (18, 19). Harris (30) also indicated that weight gain between 25 and 50 years of age was a risk factor for Type 2 diabetes, and Di Pietro et al. (31) have shown a rapid weight gain between puberty and age 25 years in a cohort of Swedish subjects who were overweight in childhood and went on to develop diabetes. As mentioned earlier, weight gain also preceded Type 2 diabetes in Pima Indians (20, 27).

An association between weight gain and Type 2 diabetes could be explained in a number of ways. It is possible that weight gain in the short-term could lead to -cell decompensation, glucose intolerance and Type 2 diabetes in already susceptible individuals, with the BMI at which this occurs determined by other factors such as genotype, age or physical activity. Alternatively, weight gain could be a result of the hyperinsulinaemia that precedes Type 2 diabetes (32±34). Thirdly, behavioural factors resulting in weight gain, such as dietary changes or reduced physical activity may also promote the development of Type 2 diabetes. FAT DISTRIBUTION Anthropometric measures of body fat distribution (e.g. waist=hip ratio (WHR), subscapular=triceps skinfold ratio (STR), waist=thigh ratio) or computed tomography (CT) scan measures are associated with risk of diabetes, both in longitudinal (14 ± 17) and cross-sectional (2, 6 ±13) studies. The effects of fat distribution are generally independent of measures of overall fatness (2, 6± 8, 10, 11, 13 ±17), and in many cases fat distribution appears more important (6 ± 11, 20). Is Fat Distribution More Important than Overall Fatness? Shelgikar et al. (10) found that WHR was more strongly associated with the prevalence of impaired glucose tolerance and diabetes than was BMI in Asian Indians in India. McKeigue et al. (11) confirmed the greater importance of WHR for prevalence of glucose intolerance in Asian Indian and European men and women in London. Waist= thigh ratio was also more strongly associated with Type 2 diabetes than was BMI in Pima Indians, especially those between 25 and 34 years of age (20). Sosenko et al. (35) examined the usefulness of BMI, WHR, and STR as screening tools for Type 2 diabetes in a group of Mexican American and nonHispanic white volunteers. Both WHR and STR were better markers of Type 2 diabetes than was BMI. With increasing age, the associations with both WHR and BMI were attenuated. Longitudinal data are less consistent. Among men of the Normative Aging Study who were

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

followed over 18 years, fat distribution as measured by the ratio of abdominal circumference=hip breadth was a stronger predictor of both Type 2 diabetes and IGT than was BMI (17), but in prospective studies of Swedish men (14), and women (15), BMI and WHR were of similar importance. The tendency for markers of fat distribution to be more strongly associated with Type 2 diabetes prevalence than is BMI could be explained if a fall in BMI but not WHR was associated with the onset of diabetes. This hypothesis is supported by data from Mauritius. Figure 19.1 (A & B) shows a decrease in BMI but not WHR over 5 years in people with newly diagnosed or known diabetes. Gender, Fat Distribution and Diabetes Consideration of the literature as a whole suggests that both overall adiposity and fat distribution are independently important risk factors for Type 2 diabetes, in both men and women (2, 6± 8, 10, 11, 13 ±17). However, some studies suggest gender differences in the relative importance of overall fatness and fat distribution. In a study of Type 2 diabetes prevalence in Mauritius, Dowse et al. (6) found that WHR and BMI were independently associated with Type 2 diabetes, after controlling for age, family history of diabetes and physical activity. The effect of WHR was greater than that of BMI in women, while in men the converse was found. In Brazilian and Chinese adults BMI and WHR were independently associated with Type 2 diabetes in women but only WHR remained significant in multivariable analysis in men (7, 8). Haffner et al. (36) found that overall obesity was independently associated with Type 2 diabetes prevalence in Mexican American and non-Hispanic white men and women, but central obesity (subscapular=triceps skinfold ratio, STR) was only significant in women. This led them to propose a plateau effect of centrality, whereby above a certain level of STR, i.e. that achieved in most men, there was no further increase in rates of Type 2 diabetes. Fat deposition in men is generally abdominal, thus waist circumference or WHR would be expected to correlate strongly with overall obesity, reducing the chance of both measures being significant in multivariable analyses. In women

there is more variation in fat distribution, so measures such as WHR would be expected to differentiate between higher and lower risk individuals within a given level of overall fat mass, and it is therefore more likely to be independently significant. This is shown in Mauritius where correlation between BMI and WHR was much stronger in men (r = 0.45) than in women (r = 0.28). Similar results were also found in Nauruans (37). The lack of independent association of Type 2 diabetes prevalence with fat distribution in men may therefore be due to the limited range in abdominal obesity, or its correlation with overall obesity, rather than the higher degree of abdominal obesity. Interestingly, waist=hip ratio has been observed to be a better indicator of CT measured abdominal fat in men than women (38), suggesting that the association of WHR with metabolic aberrations including Type 2 diabetes, should in fact be stronger in men. However, as discussed above, this does not appear to be the case. Fat Distribution, Overall Fat Mass and Type 2 Diabetes Evidence for a greater effect of fat distribution at higher levels of overall obesity is available from a number of sources. In a study of over 15 000 women attending weight loss groups, the selfreported prevalence of diabetes was associated with both WHR and BMI but the increase in prevalence across tertiles of WHR was steeper in the most obese group (12) suggesting an interaction between BMI and WHR. Among the Swedish men followed up by Ohlson et al. (15), the incidence of Type 2 diabetes was higher for each successive tertile of WHR within each tertile of BMI, but the increase in incidence with increasing WHR was only about 6-fold in the lowest BMI tertile compared with a 30-fold increase in the top tertile of BMI. Schmidt et al. (8) found that the association of WHR with Type 2 diabetes prevalence was 1.5 times stronger in obese women compared with lean, while the reverse situation was found in men. In contrast, data from the Pima Indians indicate that the association of Type 2 diabetes with fat distribution weakened with increasing BMI or age (20), while in each of Indian, Creole and Chinese Mauritians there was

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TYPE 2 DIABETES AND OBESITY

no evidence for any interaction between BMI and WHR (6). The apparently greater effect of WHR in more obese subjects appears in contrast to the results of Busetto et al. (38), who showed that the relationship between WHR and intra abdominal fat area was stronger in lean subjects compared with obese. If WHR is important because it is a marker of intra-abdominal fat, its association with metabolic disturbances might be expected to be stronger in lean subjects, where it is a more precise marker. In more specific studies Bonora et al. (39) measured glucose tolerance in 18 normal weight and 18 obese women in relation to WHR, STR or visceral=subcutaneous abdominal fat ratio by CT scan. Fasting glucose utilization did not vary between the two groups, but during euglycaemic insulin clamp studies only total body fat was related inversely to glucose utilization in the lean women, while in the obese only fat distribution (inversely), and not total fat was important. Landin et al. (40) also found that the effect of WHR was greater in obese than lean women. Similarly in men, Pouliot et al. (41) found that fat distribution by CT scan was related to glucose and insulin metabolism only in obese men (n = 58) and not in lean men (n = 29). These results suggest that a reasonably high level of overall obesity is required to facilitate the effects of central obesity. On the other hand, in extremely obese subjects with a tendency to abdominal adipose distribution, increases in adiposity beyond a certain point may result in fat being accumulated in other areas, leading to a proportional reduction in intra-abdominal fat in association with increasing BMI. The risk of Type 2 diabetes may continue to increase as body fat content rises, weakening the association between fat distribution and Type 2 diabetes. Such a scenario could be envisaged in the Pimas and observations in Western Samoans support this hypothesis. In summary, both overall obesity and fat distribution contribute to the risk of Type 2 diabetes, but their relative importance appears to vary in relation to whether incidence or prevalence of Type 2 diabetes is used, the gender of the individuals examined or their degree of obesity. However, strategies to reduce Type 2 diabetes risk via diet and physical activity can reduce both overall and abdominal obesity and improvements in both should be sought.

GENETIC SUSCEPTIBILITY, OBESITY AND TYPE 2 DIABETES Genetic susceptibility to Type 2 diabetes can be studied at the population level, where it is believed that certain ethnic groups such as Native Americans and Micronesians (Nauruans) have enhanced susceptibility to Type 2 diabetes (24); or at the personal level where family history of Type 2 diabetes is a well-recognized risk factor (1, 4, 6, 24, 27, 42). Family History Fujimoto et al. (43) observed similar levels of both general adiposity and fat distribution across normal and Type 2 diabetes men with a positive family history of diabetes, while in men with no family history of diabetes those with Type 2 diabetes were fatter. This was supported by the findings of Lemieux et al. (44). In this study 104 men were divided first into those with `normal' or `altered' insulin and glucose during an oral glucose tolerance test. Among men with `high' levels of insulin or glucose or both, those with a negative family history of diabetes had a higher BMI at age 20, and a lower current ratio of abdominal=thigh adipose tissue than men with normal glucose metabolism. These two studies suggest that a higher level of obesity is required for the development of diabetes in individuals without a genetic predisposition to Type 2 diabetes. Consistent with this, Kuzuya and Matsuda found that patients with Type 2 diabetes who had a definite history of obesity had a lower prevalence of family history of diabetes than those who had not been obese (45). Similarly, the siblings of lean diabetics had a higher prevalence of Type 2 diabetes than the siblings of obese diabetics in the study of Lee et al. (46), and Hanson et al. (47) observed that the prevalence of Type 2 diabetes in Pimas was higher in relatives of leaner Type 2 diabetes cases than in relatives of more obese cases. In the latter three studies it appears that leaner cases of Type 2 diabetes did not need to accumulate as much adipose tissue as obese diabetics because they had a greater familial predisposition contributing to Type 2 diabetes risk. Among elderly men in the Zutphen study, obesity did not appear to be related to diabetes, irrespective of family history

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

Figure 19.3 Prevalence of Type 2 diabetes (non-insulin-dependent diabetes mellitus) by body mass index (BMI) and family history of Type 2 diabetes, in men and women of three populations at high risk of Type 2 diabetes

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TYPE 2 DIABETES AND OBESITY

(48). However, it is possible that these associations change with age and that other risk factors, such as an age-related deterioration in insulin sensitivity, become more important. In an earlier study of Pima Indians, a clear interaction was demonstrated between family history and BMI in relation to the incidence of Type 2 diabetes, such that a positive family history enhanced the effect of BMI on Type 2 diabetes risk (27). Such an interaction has not been observed in other populations. Ohlson et al. did not find a significant interaction in Swedish men who were followed for 13.5 years (42), and for data from Western Samoa, Nauru and Mauritius (Figure 19.3), it was only in Nauruan women that the interaction of family history and BMI was significantly ( p = 0.023) associated with Type 2 diabetes prevalence. Ethnic Group Among the biracial population of the San Antonio Heart Study (49), it is clear that the prevalence of Type 2 diabetes is higher in Mexican Americans than in non-Hispanic whites. This difference may be attributed to a number of factors, including the greater degree of obesity among Mexican Americans. However, even within each of three levels of obesity (based on sum of skinfolds in nonHispanic whites) the prevalence of Type 2 diabetes was higher in Mexican Americans, suggesting increased genetic susceptibility, or increased levels of other risk factors. A more recent study among similar racial groups in Colorado also indicated that the risk of Type 2 diabetes was higher in Hispanics than in non-Hispanic white Americans, independent of BMI and other risk factors (1). Asian Indians also appear to have an elevated risk of Type 2 diabetes compared to members of other ethnic groups at similar or lower levels of BMI (50±52). When these studies were conducted the measurement of body fat distribution was not as common as it is today, but in Fiji it was observed that although Indians had lower BMI than Melanesians, their triceps skinfold thicknesses were greater, consistent with greater muscularity among Melanesians (52). Shelgikar et al. have since shown that for Asian Indians, WHR is more important than BMI in defining risk of Type 2 diabetes in a cross-sectional study (10). This was confirmed by

McKeigue et al. (11) in a study which also found that Indians had a greater WHR than Europeans for the same level of BMI. Thus the differential distribution of body fat appears to play a role in the increased susceptibility of Asian Indians to Type 2 diabetes and cardiovascular disease (53). There is evidence to suggest that in some other ethnic groups the risk associated with a central distribution of body fat is relatively low. Among non-Hispanics in Colorado the diabetes risks associated with BMI, triceps and subscapular skinfold thicknesses, family history and income were similar to those found in Hispanics. However, a 1 unit increase in either WHR or STR was associated with a greater risk of Type 2 diabetes among non-Hispanic whites than among Hispanics (1). Similarly, upper body obesity was more closely associated with increased concentration of insulin and glucose in the blood, and reduced insulin sensitivity by the minimal model method, in obese Caucasian compared to African American women (54). This metabolic study, along with the study of Marshall et al. (1) suggest that Caucasians may be more susceptible to the effects of fat distribution than some other ethnic groups. PHYSICAL ACTIVITY, OBESITY AND TYPE 2 DIABETES Obesity and physical activity have been found to be independently associated with both prevalence (6, 55) and incidence (56 ± 60) of Type 2 diabetes in men and women. Physical activity may lower Type 2 diabetes risk via reduced total body fat (55, 56, 58 ±62) and less abdominally distributed fat (55, 58, 61, 62), and=or through its action in improving insulin sensitivity (61, 62). These mechanisms are closely linked, but the independent effects of activity and obesity suggest that physical activity can modify the risk of Type 2 diabetes associated with a given level of obesity. MECHANISMS LINKING OBESITY AND TYPE 2 DIABETES Thrifty Genotype Neel (63, 64) argued that a disease such as diabetes which appears to reduce reproductive rates must

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

have, in the past, conferred some survival advantage to become so prevalent. Neel's thrifty genotype hypothesis suggests that a metabolism adapted to survival under `feast or famine' conditions, by efficient storage and utilization of energy, would lead to obesity and Type 2 diabetes under conditions of ample food and lower physical activity (63). The high rates of obesity and Type 2 diabetes among indigenous populations such as American Indians and Pacific Islanders who have undergone rapid modernization, are thought to be consistent with the thrifty genotype hypothesis (65). Hyperinsulinaemia, and=or relative insulin resistance in skeletal muscle may be the basis for obesity and the vicious cycle of increasing insulin resistance and compensatory hyperinsulinaemia that eventually lead to -cell failure and Type 2 diabetes (65). However, recent studies have shown that low insulin levels and relative insulin sensitivity increase the risk of weight gain (66 ± 69), and that insulin resistance may in fact act to limit weight gain in obese individuals (68). Careful longitudinal studies are required in order to increase our understanding of the metabolic pathways leading to obesity and glucose intolerance in humans (66). Animal studies have provided evidence for common genetic causes for obesity and glucose intolerance, but the situation in humans is likely to be more complex. Single gene mutations in the ob=ob and db=db mice lead to early weight gain and glucose intolerance (70). The Israeli sand rat (Psammomys obesus) is used as a model of the thrifty genotype. In the wild, sand rats living on a diet of saltbush remain lean and normoglycaemic. However, on a diet of laboratory chow sand rats develop obesity, insulin resistance, glucose intolerance and diabetes (71). Leptin Leptin, the product of the ob gene, has been shown to reduce energy intake and body weight in ob=ob mice (72 ±75), but not in db=db mice (72, 74). A truncated form of leptin is produced by adipose tissue in ob=ob mice (76), while db=db mice appear to have a defect in the hypothalamic leptin receptor (77, 78) and have relative hyperleptinaemia (78). Obese humans (79 ± 81) and sand rats (82) also have elevated leptin levels, consistent with a

receptor defect. Such a defect could be part of the expression of the thrifty genotype. CONCLUSION Obesity is clearly associated with an increased risk of Type 2 diabetes, and the longer the duration and greater the degree of obesity, generally the higher the risk. Prospective studies show a stronger relationship between obesity and Type 2 diabetes than cross-sectional studies because the onset of Type 2 diabetes tends to be associated with weight loss. Waist=hip ratio on the other hand, may remain high after Type 2 diabetes is diagnosed, thereby showing a stronger association than does BMI in cross-sectional studies. Overall body mass and abdominal fatness are more likely to contribute independently to the risk of Type 2 diabetes in women than in men, possibly because the two measures are more strongly correlated in men than in women. Familial propensity to Type 2 diabetes and obesity are two of the factors that contribute to risk of Type 2 diabetes. Where Type 2 diabetes is associated with a strong family history of diabetes, it may occur at a lower level of obesity. Ethnic groups also differ in the degree of diabetes risk associated with similar levels of obesity, whether due to genetic susceptibility or the presence of other risk factors. Obesity and Type 2 diabetes may be manifestations of the same thrifty genotype, characterized by insulin resistance and hyperinsulinaemia, but the likelihood of a simple genetic explanation seems low. Single gene defects in the leptin receptor or the leptin molecule appear to cause both obesity and glucose intolerance in animal models. The role, if any, of leptin in producing obesity and Type 2 diabetes in humans remains to be elucidated. REFERENCES 1. Marshall JA, Hamman RF, Baxter J, Mayer EJ, Fulton DL, Orleans M et al. Ethnic differences in risk factors associated with the prevalence of noninsulin-dependent diabetes mellitus. Am J Epidemiol (1993); 137: 706± 718. 2. Haffner SM, Stern MP, Hazuda HP, Pugh J, Paterson JK. Do upper-body and centralized adiposity measure different aspects of regional

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Epidemiology of the Insulin Resistance Syndrome Beverley Balkau and Eveline EschweÁge

INSERM U21 Faculty of Medicine Paris-Sud, France

INTRODUCTION The insulin resistance syndrome, otherwise known as the plurimetabolic syndrome or `Syndrome X' as formalized by Reaven (1), has provided a unifying hypothesis for the genesis of cardiovascular disease (CVD), essential hypertension and non-insulindependent (Type 2) diabetes. Using clinical, experimental and epidemiological evidence, Reaven hypothesized that chronic hyperinsulinaemia was a response to the resistance to insulin-stimulated glucose uptake (1): this hyperinsulinaemia may prevent the frank decompensation of glucose homeostasis, but it is also associated with (perhaps causally), glucose intolerance, dyslipidaemia (in particular hypertriglyceridaemia and hypo-HDLcholesterolaemia) and elevated blood pressures; these abnormalities constituted Reaven's first definition of `Syndrome X'. Zimmet and BjoÈrntorp 1988 – Reaven

insulin resistance hyperinsulinemia impaired glucose tolerance hyper VLDL triglycerides hypo HDL-cholesterolemia hypertension

1989 – Zimmet 1990 – Björntorp

central adiposity

1990 – Haffner

microalbuminuria

1993 – Reaven

hyperuricemia plasminogen activator inhibitor 1 (PAI-1) obesity

1995 – Haffner

small dense LDL

Figure 20.1 The insulin resistance syndrome

observed that the characteristics of this syndrome were also associated with abdominal adiposity (2, 3). More recently, Reaven extended DeFronzo's `triumvirate: beta-cell, muscle, liver: collusion responsible for non-insulin dependent diabetes' (4) to include the `fourth Musketeer'Ðthe adipose tissue (5). Reaven also included additional elements in the syndrome: hyperuricaemia, plasminogen activator inhibitor 1 (PAI-1) and obesity (6). The list of anomalies associated with the syndrome continues to grow: microalbuminuria has been suggested (7), and small dense low-density lipoprotein (LDL) has also been proposed (8). Reaven's unifying hypothesis created a lot of interest, and provided coherent arguments in agreement with previous observations of researchers such as Himsworth, who proposed that diabetic patients should be classed as either insulin-sensitive or insulin-insensitive (9); Vague, who noted the association between central adiposity, diabetes and atherosclerosis (10); Crepaldi, who described the `plurimetabolic' syndrome; and Modan, who concluded that insulin resistance and=or hyperinsulinaemia may be the link between hypertension, obesity and glucose intolerance (11). DEFINITION OF THE INSULIN RESISTANCE SYNDROME While there is agreement that there is a clustering of abnormalities in the insulin resistance syndrome, the elements included in epidemiological studies describing the syndrome almost always include insulin, glucose, triglycerides, HDLcholesterol and blood pressures; the other elements differ from study to study.

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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Further, there is no consensus as to the level at which each of the elements constitutes an abnormality. Often when the syndrome is described, an abnormality for a given element is defined according to the percentiles of the observed distribution or according to threshold values; sometimes these are defined by consensus groups for the treatment of the various diseases such as diabetes, hypertension, dislipidaemia. When does an individual have the syndrome? This can be defined in a number of ways, for example * * *

all the constituent elements elevated at least two of the elements of the syndrome elevated two or three specific elements elevated

Some authors have used a scoring procedure to quantify the syndrome, counting the number of abnormalitiesÐusing their own definition of an abnormality and then giving an equal weight to each of the abnormalities (12, 13). The BIGPRO clinical trial aimed to select insulin-resistant but non-diabetic subjects on the basis of central adiposityÐa waist=hip ratio of æ0.95 for the men age 35±60 years, and æ0.80 for the women aged 40±65 years (14, 15); these criteria resulted in the selection of subjects who were not only centrally obese but also obese, with an average BMI of 33.1 kg=m2, hyperinsulinaemic with a mean fasting insulin of 96 pM, but with mean lipid concentrations and arterial pressures within the normal range (15). The insulin resistance syndrome was evident: 64% of these subjects had hyperinsulinaemia (see definitions in Figure 20.2), 58% had two or more of the five measured abnormalities of the syndrome, only 15% had no abnormality (16). The final definition of the syndrome will only be established once the pathophysiology is understood. MEASURING THE ELEMENTS IN THE INSULIN RESISTANCE SYNDROME Evaluating Insulin Resistance Insulin resistance is the key element in the syndrome. While the euglycaemic hyperglycaemic clamp has become the gold standard for the measurement of insulin resistance (17), the frequently sampled intravenous glucose tolerance test

(FSIGTT) with minimal model analysis (18) is also used, even if the physiological relevance of these techniques might be disputed (19). While the clamp method may be feasible for explanatory clinical studies, the sophistication and the cost of the technique, as well as the number of subjects often included in epidemiologic studies, make it difficult in practical terms. The measures of insulin sensitivity provided by these two methods are not identical. Saad, using an insulin modified FSIGTT, found correlation coefficients between the clamp method and the 22 or 12 sample minimal models of 0.53 or 0.53 respectively, in normal glucose-tolerant subjects, and for the impaired glucose tolerant, 0.48 or 0.58 (20, 21). The correlation coefficients were lower in the diabetic subjects, 0.41 and 0.30 respectively. A number of authors have looked at the question as to which simple index of glucose and insulin concentrations (at fasting and during a 2 hour oral glucose tolerance test) best reflects insulin resistance as measured by the euglycaemic hyperinsulinaemic clamp technique and by frequently sampled intravenous tolerance tests. The literature provides a partial response, although the correlations are far from perfect (Table 20.1). In non-diabetic subjects, clamp studies have shown correlation coefficients between insulin sensitivity and fasting insulin ranging from 0.25 to 0.68, with higher coefficients in the normal glucose tolerant than in the impaired glucosetolerant subjects (Table 20.1). In the one clamp study where the 2 hour insulin concentration was measured, the correlation was a little higher than for fasting insulin, except in the diabetic subjects, where it was halved (22). Insulin sensitivity was measured by the intravenous tolerance test by Kahn and by Phillips and the correlation coefficients with fasting insulin concentrations were of a similar order to that from studies with the clamp (19, 25). Phillips also gave correlation coefficients with the product of fasting insulin and glucose, an index of insulin resistance derived from the HOMA (Homeostasis Model Analysis) (28) (Table 20.1). For impaired glucosetolerant subjects, the 2 hour insulin was the most correlated measure. There was a series of letters in Lancet which discussed the `empirical fasting insulin resistance index' (FIRI): the product of fasting plasma insulin and glucose normalized to have means values of 5

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287

Table 20.1 Pearson correlation coefficients between measures of insulin sensitivity and various parameters from a 2 hour oral glucose tolerance test, using insulin and glucose concentrations at fasting, 60 min and 120 min (I0, I60, I120, G0, G120)

Normal glucose-tolerant Laakso, * 1993 (22) Saad, 1994 (20) Phillips, * 1994 (19) Anderson, 1995 (21) Cleland, * 1996 (23)

I0

I60

0.68 0.61 0.57

0.58

I120

G0

G120

0.47 0.31 0.37

0.39

0.46

0.38

Anderson, 1995 (21)

0.25

Non-diabetic subjects Bogardus, 1989 (24) Kahn, * 1993 (25)

0.66 0.73

0.61

FIRI * * = 0.67 0.60

0.51 0.56

0.54 0.62

0.39

0.40

0.42

0.29 0.27

Duncan, 1995 (26)

FIRI = 0.79

Del Prato, 1996 (27)

FIRI = 0.035

Non-insulin dependent diabetes Laakso, * 1993 (22) 0.56 Saad, 1994 (20) 0.54 Anderson, 1995 (21) 0.55

I0=G0

0.74

0.53

Impaired glucose-tolerant Laakso, * 1993 (22) Saad, 1994 (20) Phillips, * 1994 (19)

I0  G0

0.27

Method to determine sensitivity Hyperinsulinaemic euglycaemic clamp Hyperinsulinaemic euglycaemic clamp % decline in glucose per min, 3 ± 15 min after a short intravenous insulin tolerance test Hyperinsulinaemic euglycaemic clamp Hyperinsulinaemic euglycaemic clamp Hyperinsulinaemic euglycaemic clamp Hyperinsulinaemic euglycaemic clamp % decline in glucose per min, 3 ± 15 min after a short intravenous insulin tolerance test Hyperinsulinaemic euglycaemic clamp Hyperinsulinaemic euglycaemic clamp Intravenous glucose tolerance test, Bergman minimal model Intravenous glucose tolerance test, Bergman minimal model Hyperinsulinaemic euglycaemic clamp

0.23 0.59

Hyperinsulinaemic euglycaemic clamp Hyperinsulinaemic euglycaemic clamp Hyperinsulinaemic euglycaemic clamp

* Laakso and Phillips: logarithms of insulin and glucose. Kahn: logarithms of both insulin and the insensitivity sensitivity index. Cleland: logarithm of FIRI. * * FIRI = empirical fasting insulin resistance index = I0x G0=25, I0: mU=ml, G0: mM both normalized to have mean values of 5.

mU=l and 5 mM respectively, with a reference range centred around unity: FIRI = (G0  I0)=25 (23, 26, 27). Duncan found in normal glucosetolerant subjects a correlation of 0.79 with insulin sensitivity, estimated from the minimal model (26). However, using a 120 min euglycaemic, hyperinsulinaemic clamp methodology, Del Prato found a correlation of only 0.035 (27). In contrast, Cleland, using a 180 min euglycaemic, hyperinsulinaemic clamp, found a correlation of 0.67, using the logarithm of the index (23). The difference between these two latter studies may be because of the length of the clamp and the fact that Del Prato did not take logarithms for the correlation coefficient. An additional problem is the assay method for insulin concentrations Ð it is not always clear whether the above studies used an assay specific for insulin. Phillips assayed insulin, split proinsulin and proinsulin (19); while insulin correlated well with insulin sensitivity, r ˆ 0:57 and r ˆ 0:37 respectively for normal and impaired glucosetolerant subjects, split proinsulin (r ˆ 0:50,

0:62 respectively) had higher correlations than proinsulin (r ˆ 0:36, 0:25). Thus, while split proinsulin might be a useful additional parameter to evaluate insulin resistance in impaired glucosetolerant subjects, in normal subjects the correlation with insulin was equally good. In summary, fasting insulin is a surrogate measure of insulin resistance in the normoglucose-tolerant subject, but it does not perform as well in either the impaired glucose-tolerant or in non-insulin-dependent diabetic subjects (29). The FIRI measure requires further investigation. Even if it were possible to perform the clamp or the intravenous glucose tolerance test methodology in epidemiological studies, there is no agreed limit for `insulin insensitivity'. Hyperinsulinaemia For insulin, the variability between assays would render the definition of a universal threshold

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difficult (30). However, for a given study, the laboratory quality control should ensure that the values are internally consistent. The nonspecificity of the great majority of insulin assays has been called into question. Haffner (31) showed that in non-diabetic Mexican Americans and nonHispanic whites, the ratio of pro-insulin to insulin was 7% in normal glucose-tolerant subjects, in impaired glucose-tolerant subjects 9%, and much higher in the diabetic subjects (31%). Given the great variability in insulin assays between sites, this would appear to be of minor importance in the non-diabetic subjects. Wherever fasting insulin concentrations or those following an oral glucose tolerance test (and at what time interval) were used has not been mentioned in the definition of the syndrome, it has usually been assumed that the basal or fasting rate is to be used.

Impaired Glucose Tolerance The biological assays for glucose may be sufficiently standardized now, so that glucose levels can be compared across studies. The World Heath Organization definition for `Impaired glucose tolerance', namely a 2 hour plasma glucose concentration æ7:8 mM (140 mg=dl) following a 75 g oral glucose tolerance test (32), is presumably the reference intended by Reaven. Given that about 30% of subjects with impaired glucose tolerance may eventually progress to diabetes (33), perhaps the criteria for this element could be lower. Recent data from the Hoorn Study gave a very high annual progression rate from impaired glucose tolerance to diabetes, of 14% per year in 50 ±75year-old subjects (34); there was a marked increase in the incidence for baseline 2 hour glucose concentrations above 9.4 mM (170 mg=dl), with a conversion rate of almost 30%. This high progression rate may be in part due to the fact that the impaired glucose-tolerant subjects were those identified following two oral glucose tolerance tests Ð thus the subjects are likely to be at a higher risk. In middle-aged men from the Paris Prospective Study the risk of diabetes increased exponentially with increasing 2 hour glucose greater than 6.1 mM (110 mg=dl) (35), see Figure 20.2, even though the conversion rates were not high.

Two hour glucose concentrations following an oral glucose tolerance test are not always available from epidemiologic studies. If the diagnosis of diabetes is based on the specific complications of diabetes, retinopathy and nephropathy, then fasting glucose, 2 hour glucose and HbAlc have been shown to be equally predictive of the progression of non-diabetic subjects to these diabetic complications in Pima Indians (36). In the Paris Prospective Study the risk of diabetes would appear to increase at a faster rate for fasting than for 2 hour glucose (35, 37). Criteria for fasting rather than post-load glucose concentrations need to be agreed for the definition of diabetes, `pre-diabetes' and for the inclusion of fasting glucose in the definition of the insulin resistance syndrome. The American Diabetes Association proposed recommendations in June 1996 were, for fasting glucose, diabetes: æ7:0 mM (125 mg=dl), and `hyperglycemia': 5.8 to 6.9 mM (105 ± 124 mg=dl) (38). While in individual studies, HbA1c levels may be predictive of decompensation to diabetes, and so to an insulin-resistant state, the assays for HbAlc are not as yet sufficiently standardized to be able to give a universal definition. Hypertriglyceridaemia, Hypo-HDL-cholesterolaemia Dyslipidaemia is not a disease, but a risk factor for cardiovascular disease and so any available threshold values are for treatment for the prevention of cardiovascular disease. However, while the evidence for HDL-cholesterol as a cardiovascular risk factor is not disputed, it is debated whether triglycerides are a risk factor, and whether it is only a risk factor when the HDL-cholesterol concentration is low (39). Triglyceride concentrations are subject to a high biological variation, even in the fasting state. It is recommended that fasting should be for a period of 9±12 hours, and blood drawn after the subject has been seated for 5 minutes (40). For the treatment of hypertriglyceridaemia the European guideline (41) for both hypertriglyceridaemia and for mixed dyslipidaemia was a triglycerides concentration >2.3 mM (200 mg=dl). In the United States the NIH classified 2.8±5.6 mM (250±500 mg=dl) as borderline, and subjects with æ5:6 mM (500 mg=dl)

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289

Figure 20.2 Incidence of diabetes according to fasting and 2 hr glucose concentrations. The Paris Prospective Study

as having definite hypertriglyceridaemia (40), while the Adult Treatment Panel of the National Cholesterol Education Program (NCEP) used 2.3±4.6 mM (200 400 mg=dl) as borderline, and >4.6 mM (200 mg=dl) as high or very high triglycerides (40). While the thresholds for treatment of dyslipidaemia are for fasting concentrations, post-prandial values may be equally predictive as a risk factor for cardiovascular disease, but a standardized meal would be necessary (42). The criteria for treatment for HDL-cholesterol from the American NCEP Expert Panel (43) were 30 kg=m2. Microalbuminuria (urinary albumin excretion rate æ20 mg=min or albumin : creatinine ratio æ30 mg=g).

This definition has been criticised (122) and one of the main problems is that it requires a direct measure of insulin resistance, either by a euglycaemic hyperinsulinaemic clamp (17) or by a frequency sampled intravenous glucose tolerance test and the minimal model (18), measures which are rarely available in epidemiological studies. To date, this definition has been little used. One of the few studies that has looked at the development of the syndrome, comes from the Atherosclerosis Risk in Communities (ARIC) Study (123). The syndrome was defined by diabetes, hypertension and dyslipidemia. Subjects who had a greater number of these anomalies after three years of follow-up had, at baseline, high insulin levels, BMl, waist ± hip ratio and abdominal girth. Finally, one of the more promising insulin sensitising drugs, Troglitazone, was withdrawn from the Diabetes Prevention Program due to liver toxicity (124). However, two newly launched members of the same thiazolidinedione family (Rosiglitazone and Pioglitazone) do not seem to be associated with the same liver toxicity. There is an ongoing surveillance of this side effect.

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21A

Long-term Complications: Diabetes and Coronary Heart Disease Elizabeth Barrett-Connor1 and Kalevi PyoÈraÈlaÈ2 1

University of California, San Diego USA 2 University of Kuopio, Kuopio, Finland

Cardiovascular disease (CVD), which includes coronary heart disease (CHD), stroke, and peripheral arterial disease (PAD), is the major cause of morbidity and mortality in patients with insulindependent or non-insulin-dependent diabetes (Type 1 diabetes or Type 2 diabetes, respectively). Although diabetes is as powerful a risk factor as cholesterol, blood pressure, or cigarette smoking, the diabetes± CVD association received relatively little systematic study until the 1979 publication of Kelly West's monumental book, Epidemiology of Diabetes Mellitus and its Vascular Complications (1) and the development of standard (WHO[2] and NDDG[3]) criteria for the definition of diabetes in the 1980s. The literature on the association between diabetes and CVD has increased exponentially in recent years. Nevertheless, there are still relatively few studies where the population characteristics and response rate are provided, the diagnosis of diabetes is based on standard criteria, the definition of CVD is explicitly stated, and there is an internal non-diabetic comparison group. In addition, there are many reasons why interpretation of the epidemiologic literature on the diabetes ±CVD association may be difficult. Both conditions are common and therefore may occur together by chance. Their association is known to clinicians, so that diagnosis of one condition often leads to a search for the other, which inflates the frequency of the association, and may do so differentially in different population groups or countries with different access to health care. Persons with known diabetes, who represent only half of all persons with Type 2 diabetes in the United States (4) tend to have more severe diabetes

of longer duration; therefore, studies based on subjects with clinically recognized diabetes are likely to yield falsely elevated estimates of macrovascular complication rates. In addition, patients are more apt to be tested for diabetes if they are obese or have other heart disease risk factors. Both Type 2 diabetes and CHD may remain subclinical for years; CHD is more often silent in diabetics than non-diabetics (5, 6). Consequently, both within-population and cross-cultural comparisons of the Type 2 diabetes ±CHD association should be interpreted cautiously unless they are population-based and unless both diabetes and heart disease have been sought in the whole study cohort Ðfor example, by obtaining glucose tolerance tests and electrocardiograms. While Type 1 diabetes is less likely to be subclinical, most studies of Type 2 diabetes and CHD are patient-based case series derived from specialty clinics, where patients with more severe diabetes and more complications may selectively accrue. This review has focused primarily on populationbased studies with internal comparison groups. It will be seen that few studies do not have at least some of the potential biases described above. CORONARY HEART DISEASE Prevalence Studies Several recent North American studies have reported the prevalence of coronary heart disease in adults who received a standard oral glucose tolerance test (OGTT), as shown in Table 21A.1 (5, 7 ± 10). As shown, the CHD prevalence rates

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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Table 21A.1 Age-adjusted prevalence of coronary heart disease among adults by oral glucose tolerance status, North American studies Population (Ref.)

Race

Sex

Glucose tolerance Status

Rancho Bernardo, CA (population-based, 1984 ± 87, 50 ± 89 years) (5)

White

San Luis Valley, CO (population-based, 1984 ± 88, 25± 74 years) (7)

White

M F M F

Mexican American

M F

San Antonio, TX (population-based, 1979 ± 88, 25± 64 years) (8)

King County, WA (volunteers, 1983 ± 85, 45± 74 years) (9) King County, WA (volunteers, 1986 ± 88, 45± 74 years) (10)

White

M F

Mexican American

M

Japanese American

M

Japanese American

F

F

T2DM IGT Normal T2DM IGT Normal T2DM ICT Normal T2DM IGT Normal T2DM IGT Normal T2DM IGT Normal T2DM Other T2DM Other T2DM Other T2DM Other T2DM IGT Normal T2DM IGT Normal

Myocardial infarction (%)

Ischemic ECG (%)

Number

History

Hx or ECGa

Totalb

Silentc

159 237 591 157 347 732 79 34 307 66 48 345 107 35 214 177 56 226 37 790 41 995 154 1230 240 1654 78 72 79 52 67 72

Ð Ð Ð Ð Ð Ð 12.3 * 11.6 4.8 4.4 1.4 2.0 5.6 8.8 6.7 2.8 2.5 0.4 13.3 4.0 7.2 1.2 12.6 3.1 4.2 1.1 Ð Ð Ð Ð Ð Ð

17.1 18.4 * 13.2 13.7 * * 9.5 7.2 26.5 * 18.1 12.8 14.2 * 9.5 7.0 11.3 18.0 16.3 12.4 11.6 8.7 29.9 8.2 11.7 2.5 21.6 5.6 10.7 3.1 Ð Ð Ð Ð Ð Ð

34.7 * * 28.2 22.3 30.0 * * 23.3 19.6 16.7 19.0 9.7 25.5 * 18.3 15.5 14.2 13.3 12.5 18.5 20.9 14.9 Ð Ð Ð Ð Ð Ð Ð Ð 29.5 * * * 23.6 6.3 Ð Ð Ð

20.9 * 13.0 12.0 13.7 * 12.0 8.8 7.7 7.4 4.5 17.8 15.3 11.8 12.2 * 2.4 5.2 13.6 16.7 10.3 Ð Ð Ð Ð Ð Ð Ð Ð 16.7 * * 12.5 3.8 Ð Ð Ð

Coronary heart diseased(%)

43.1 * * 38.5 32.0 41.9 * * * 34.3 * 28.5 36.9 * 37.7 23.1 50.6 * 40.1 * 24.6 29.7 29.1 29.7 44.5 * 36.4 32.6 Ð Ð Ð Ð Ð Ð Ð Ð 41.0 * * * 27.8 8.9 36.5 * 35.8 * 19.4

ECG = electrocardiogram; T2DM = Type 2 diabetes mellitus; IGT = impaired glucose tolerance; NHW = non-Hispanic white. a History or ECG Minn. code 1.1 (5); history or ECG Minn. code 1.1 ± 1.2 (7); history or ECG Minn. code 1.1 ± 1.3 (8). b ECG Minn. code 1.1 ±1.3, 4.1 ± 4.3, 5.1 ± 5.3, 7.1 (5,7); ECG Minn. code 1.1 ± 1.3, 5.1± 5.3, 7.1 (9, 10). c Ischemic ECG without history of MI, angina, or chest pain (5, 7, 9). d Possible Ml, angina, or ischemic ECG (5); heart attack, angina, or ischemic ECG (7); Ml, bypass, angina or ischemic ECG (9, 10). d p Æ 0.05; * *p Æ 0.01; * * *p Æ 0.001 compared to normal glucose tolerance group.

for persons with and without abnormal carbohydrate tolerance vary by population, probably reflecting differences in age, ethnicity and body size. Within each population, diabetic Americans of European, Mexican and Japanese ancestry nearly always had significantly more clinically manifest CHD and more silent ischemia (as defined by resting electrocardiogram) than those with normal glucose tolerance. The absent diabetes ± CHD association in the Mexican American men from the San Luis Valley Study may be due to chance, small sample size, or prevalence bias, i.e. increased mortality in men who have both diabetes and CHD.

The Strong Heart Study provides age-adjusted prevalence rates for 13 American Indian communities in Arizona, Oklahoma and the Dakotas (11). The prevalence of CHD defined by history and resting electrocardiogram was higher in diabetics than non-diabetics in all groups and both sexes, but there is a significant unexplained difference in the prevalence of CHD by geographic area, as shown in Table 21A.2 (11). Results of three Finnish studies using similar methods for the diagnosis of Type 2 diabetes and CHD are shown in Table 21A.3 (12 ±14). In East Finland (12, 13) the frequency of symptomatic CHD and ischemic ECG abnormalities was

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DIABETES AND CORONARY HEART DISEASE

Table 21A.2 Prevalence rates (per 100) of coronary heart disease, by sex, study center, and diabetes status, in Native Americans; The Strong Heart Study, 1989±92 Non-diabetic Women (no.) Definite CHD Arizona Oklahoma Dakotas All p (center) * Possible CHD Arizona Oklahoma Dakotas All p (center) *

1 3 2 6 0.887

(%) 0.4 0.6 0.4 0.5

44 16.1 89 17.4 86 17.9 219 17.3 0.815

Diabetic Men

(no.) 1 19 14 34 0.026

Women (%) 0.5 4.7 3.2 3.3

18 9.8 52 12.9 68 15.6 138 13.4 0.140

(no.) 4 11 14 29 0.001

Prevalance ratio (PR) (diabetic: non-diabetic) Men

(%)

(no.)

Women (%)

0.6 3.2 4.1 2.2

5 1.5 20 8.7 19 9.6 44 5.8 200 99 9 Y 9 9 9 9 9 Y 9 9

This table is based on the following references: Type 1 diabetes and Type 2 diabetes: 16, 20, 22, 24, 43, 44, 78, 79. Non-diabetic individuals: 4, 88± 98.

Type 2 diabetes patients 20±200 9 9 Ð Ð Ð Ð 9 9

> 200 99 9 Y 9 9 9 9 9

9 9

9 9

Non-diabetic individuals > 8 (or 15) 9 Ð Y Ð Ð Ð Ð 9 Y 9 9

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DIABETIC NEPHROPATHY

with diabetic nephropathy. Many of these atherogenic risk factors are changed in the same direction in patients with microalbuminuria in Type 1 diabetes patients as well as in Type 2 diabetes patients. Recently we have been able to demonstrate similar changes in non-diabetic individuals with elevated UAER (> 10 mg=min). As seen from Table 21D.3 the changes are rather extensive with respect to the number of parameters affected, but within each risk factor the difference in the distribution between patients with and without nephropathy is modest. Our interpretation therefore, is that this aggregation of risk factors cannot by themselves explain the huge differences in the risk of developing CVD. Again, we therefore suggest a common, underlying pathogenetic mechanism for micro- and macrovascular complications as stated in the Steno Hypothesis (68). DIABETIC NEPHROPATHY ÐFUTURE PERSPECTIVES As already discussed, progression of microalbuminuria to clinical diabetic nephropathy can Ðat least in part Ðbe prevented by strict metabolic control. Furthermore, antihypertensive therapy in general decreases or normalizes the urinary albumin excretion rate. Most intervention trials with antihypertensive therapy in patients with microalbuminuria have been short-term studies (6 ± 24 months) (80 ± 82) leaving unanswered the question whether it would prevent development of diabetic nephropathy. One long-term study has shown that the risk of developing overt diabetic nephropathy is significantly lower in patients treated with an ACE inhibitor (Captopril) than with placebo (83), and this observation was confirmed in a larger study with 92 patients followed-up over 2 years (84). Thus ACE-inhibitor treatment is effective in preventing progression to diabetic nephropathy. Whether this is also the case for other antihypertensive agents remain unanswered. In patients with diabetic nephropathy the most important treatment is antihypertensive treatment. The beneficial effect was first shown by Mogensen (41) and Parving (42). In a follow-up of patients with diabetic nephropathy diagnosed before the `antihypertensive era' (1957 ± 73) and in the early `antihypertensive era' (diagnosed 1979± 83) Mathiesen et al. found that the 8-year survival

343

increased from 48 to 87% (85). This study showed that after using antihypertensive treatment as a routine in the clinic, but not as part of a controlled clinical trial, the prognosis considerably improved. In these three studies (41, 42, 85) different antihypertensive regimens were used, and a beneficial effect of lowering of blood pressure was seen independent of type of treatment. In the study by Lewis et al. (86) the aim was to obtain the same blood pressure level in the two groups, using Captopril or placebo in combination with other (non-ACE-inhibitor) antihypertensive agents. In this study there was an additional effect of ACE inhibitors compared with the control group, but whether the antihypertensive effect in itself was effective could not be analysed due to the design of the study. Thus, it is likely that control of blood pressure significantly reduces the annual rate of decline in GFR, and thereby considerably postpones the development of end-stage renal failure. In conclusion, screening for microalbuminuria and proteinuria in combination with antihypertensive therapy is the key element in the prevention of end-stage renal failure in diabetic patients. The generally accepted recommendation for treatment target is a blood pressure Æ 140=90 (87). PROSPECTS FOR FUTURE INTERNATIONAL COLLABORATIVE RESEARCH As already mentioned, the research within the field of diabetic nephropathy has been done on White, European Caucasian populations. This is true for epidemiological studies as well as for intervention studies and basic physiological and genetic studies. Thus, very little is known about the effect of ethnicity on the risk of developing nephropathy and the progression of the disease once it has developed. From epidemiological studies of cardiovascular disease it is, however, well known that the prevalence of important risk factors for progression of nephropathy as hypertension shows great variation between ethnic groups. Thus, collaborative, population-based studies using standardized protocols should be encouraged, similar to the worldwide standardized incidence registers for Type 1 diabetes. Continuous search for genetic factors associated with development of nephropathy is another area

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

which calls for international collaboration. Such studies will only be successful if sufficient numbers of informative families can be co-opted, and very few countries will have sufficient numbers of informative families to perform these studies on their own. Finally, future large-scale controlled clinical trials should take into consideration that potential treatment effects may depend on factors such as ethnicity, socio-economic status and general living conditions related to where the person is living. Therefore international collaboration in this field is also needed to ensure generalizability of the results of future trials.

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65. Jensen JS, Mathiesen ER, Nùrgaard K, Hommel E, Borch-Johnsen K, Funder J, Brahm J, Parving HH, Deckert T. Increased blood-pressure and red-cell sodium=lithium counter transport activity are not inherited in diabetic nephropathy. Diabetologia (1990); 33: 619± 624. 66. Viberti GC Keen H, Wisemann HJ. Raised arterial pressure in parents of proteinuric insulin-dependent diabetics. Br Med J (1987); 295: 515± 517. 67. Sytkowski PA, Kannel WB, Agostino RB. Changes in risk factors and the decline in mortality from cardiovascular disease. The Framingham Heart Study. N Engl J Med (1990); 322: 1635± 1641. 68. Deckert T, Feldt-Rasmussen B, Borch-Johnsen K, Jensen T, Kofoed-Enevoldsen A. Albuminuria reflects widespread vascular damageÐ The Steno Hypothesis. Diabetologia (1989); 32: 219± 226. 69. Kofoed-Enevoldsen A. Heparan Sulphate in the pathogenesis of diabetic nephropathy. Diabetes Metab Rev (1995); 11: 137± 160. 70. Kofoed-Enevoldsen A, Jensen T, Borch-Johnsen K, Deckert T. Incidence of retinopathy in Type 1 (insulin-dependent) diabetes: association with clinical nephropathy. J Diabetes Compl (1987); 1: 96±99. 71. Klein R, Moss SE, Klein BEK. Is gross proteinuria a risk factor for the incidence of proliferative diabetic retinopathy? Ophthalmology (1993); 100: 1140±1146. 72. Bodansky HJ, Cudworth AG, Drury PL, Kohner EM. Risk factors associated with severe proliferative retinopathy in insulin-dependent diabetes mellitus. Diabetes Care (1982); 5: 97 ± 100. 73. Stephenson JM, Fuller JH, Viberti GC, Sjùlie AK, Navalesi R, the EURODIAB IDDM Complications Study Group. Blood pressure, retinopathy and urinary albumin excretion in IDDM: the EURODIAB IDDM Complications Study. Diabetologia (1995); 38: 599± 603. 74. Tuomilehto J, Borch-Johnsen K, Molarius A, Forsen T, Rastenyte D, Sartill C, Reunanen A. Incidence of cardiovascular disease in Type 1 (insulin dependent) diabetic subjects with and without diabetic nephropathy. Diabetologia (1998); 41: 784±790. 75. Rewers M, LaPorte RE, King H, Tuomilehto J (for the Diabetes Epidemiology Research International Study GroupÐ DERI). Trends in the prevalence and incidence of diabetes: insulin-dependent diabetes mellitus in childhood. World Health Stat Quart (1988); 41: 179± 189. 76. Tuomilehto J, Kuulasmaa K, Torppa J. WHO MONICA project: geographic variation in mortality from cardiovascular disease. World Health Stat Quart (1987); 40: 171± 184. 77. Jensen T, Borch-Johnsen K, Deckert T. Coronary heart disease in young Type 1 (insulin-dependent) diabetic patients with diabetic nephropathy: incidence and risk factors. Diabetologia (1987); 30: 144±148.

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78. Jensen T. Albuminuria Ð a marker of renal and general vascular disease in IDDM. (Thesis) Dan Med Bull (1991); 38: 134± 144. 79. Marshall SM, Alberti KGMM. Comparison of the prevalence and associated features of abnormal albumin excretion in insulin-dependent and noninsulin dependent diabetes. Quart J Med (1989); 70: 61 ±71. 80. Marre M, Chatellier G, Leblanc H, Guyene TT, Menard J, Passa P. Prevention of diabetic nephropathy with enalapril in normotensive diabetics with microalbuminuria. Br Med J (1988); 297: 1092± 1095. 81. Melbourne Diabetic nephropathy study group. Comparison between perindopril and nifedipine in hypertensive and normotensive diabetic patients with microalbuminuria. Br Med J (1991); 302: 210±216. 82. Hallab M, Gallois Y, Chattelier G, Rohmer V, Fressinaud P, Marre M. Comparison of reduction in microalbuminuria by enalapril and hydrochlorthiazide in normotensive patients with insulindependent diabetes. Br Med J (1993); 306: 175 ± 182. 83. Mathiesen ER, Hommel E, Giese J, Parving H-H. Efficacy of captopril in postponing nephropathy in normotensive insulin-dependent diabetic patients with microalbuminuria. Br Med J (1991); 303: 81 ±87. 84. Viberti G, Mogensen CE, Groop LC, Pauls JF, for the European Microalbuminuria Captopril Study Group. Effect of Captopril on progression to clinical proteinuria in patients with insulin-dependent diabetes mellitus and microalbuminuria. J Am Med Assoc (1994); 271: 275± 279. 85. Mathiesen ER, Borch-Johnsen K, Jensen DV, Deckert T. Improved survival in patients with diabetic nephropathy. Diabetologia (1989); 32: 884±886. 86. Lewis EJ, Hunsicker LG, Bain RP, Rohde RD. The effect of angiotensin-converting-enzyme inhibition on diabetic nephropathy. N Engl J Med (1993); 329: 1456± 1462. 87. Krans HMJ, Porta M, Keen H. Diabetes Care and Research in Europe: The St Vincent declaration action programme. Implementation document. G Italiano Diabetologia (1992); 12 (suppl. 2): 1± 56.

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88. Valdorf-Hansen F, Jensen T, Borch-Johnsen K, Deckert T. Cardiovascular risk-factors in Type 1 (insulin-dependent) diabetic patients with and without proteinuria. Acta Med Scand (1987); 218: 439± 444. 89. Jensen JS, Borch-Johnsen K, Jensen G, FeldtRasmussen B. Atherosclerotic risk factors are increased in clinically healthy subjects with microalbuminuria. Atherosclerosis (1995); 112: 245± 252. 90. Jensen JS, Myrup B, Borch-Johnsen K, Jensen G, Jensen T, Feldt-Rasmussen B. Aspects of haemostatic function in healthy subjects with microalbuminuria Ð a potential atherosclerotic risk factor. Thromb Res (1995); 77: 423± 430. 91. Jensen JS, Borch-Johnsen K, Jensen G, FeldtRasmussen B. Microalbuminuria reflects a generalized transvascular albumin leakiness in clinically healthy subjects. Clin Science (1995); 88: 629 ±633. 92. Jensen JS, Borch-Johnsen K, Deckert T, Deckert M, Jensen G, Feldt-Rasmussen B. Reduced glomerular size- and charge-selectivity in clinically healthy individuals with microalbuminuria. Eur J Clin Invest (1995); 25: 608 ±614. 93. Yudkin JS, Forrest RD, Jackson CA. Microalbuminuria as predictor of vascular disease in non-diabetic subjects. Lancet (1988); ii: 530± 533. 94. Damsgaard EM, Frùland A, Jùrgensen OD, Mogensen CE. Microalbuminuria as predictor of increased mortality in elderly people. Br Med J (1990); 300: 297±300. 95. Haffner SM, Stern MP, Gruber KK, Hazuda HP, Mitchell BD, Patterson JK. Microalbuminuria. Potential marker for increased cardiovascular risk factors in non-diabetic subjects. Arteriosclerosis (1990); 10: 727 ±731. 96. Metcalf P, Baker J, Scott A, Wild C, Scragg R, Dryson E. Albuminuria in people at least 40 years old: effect of obesity, hypertension and hyperlipidemia. Clin Chem (1992); 38: 1802± 1808. 97. Winocour PH, Harland JOE, Millar JP, Laker MF, Alberti KGGM. Microalbuminuria and associated cardiovascular risk factors in community. Atherosclerosis (1992); 93: 71 ± 81. 98. Gould MM, Mohamed-Ali V, Goubet SA, Yudkin JS, Haines AP. Microalbuminuria: associations with height and sex in non-diabetic subjects. Br Med J (1993); 306: 240±242.

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21E

Long-term Complications: Diabetic Retinopathy Catherine A. McCarty, C. Alex Harper, and Hugh R. Taylor University of Melbourne, Victoria Australia

The study of the epidemiology of diabetic retinopathy has been greatly improved by the development and adoption of a standardized grading scheme. Many studies of the prevalence, incidence, and risk factors for diabetic retinopathy have been conducted and a Medline1 search generates hundreds of references. However, it is difficult to compare the results from some of the earlier studies because the methodology employed varied significantly. CLASSIFICATION The manifestations of diabetic retinopathy have been the subject of extensive study over the past three decades and examples of retinal changes associated with diabetes are shown in Figures 21E.1a through to 21E.1e. The modified Airlie House classification of diabetic retinopathy used in the Diabetic Retinopathy Study (DRS) (1) was extended for use in the Early Treatment Diabetic Retinopathy Study (ETDRS) (2). This classification was based on the findings in seven standard 30  retinal photographic fields, with reference to standard photographs of characteristic abnormalities. This classification was further modified to develop a retinopathy scale (3), which divides diabetic retinopathy into 13 levels ranging from absence of retinopathy to severe vitreous haemorrhage. This classification has become the gold standard for subsequent epidemiological study, including the Diabetes Control and Complications Trial (DCCT) (4). Simplified classifications of diabetic retinopathy are used by clinicians to grade disease severity, usually on the basis of findings from ophthalmo-

scopic examination. The simplified classification shown in Table 21E.1 is now widely accepted (5). Previous terminology is included in brackets. The microaneurysm is the hallmark of retinal microvascular change in non-proliferative diabetic retinopathy. Mild non-proliferative changes will usually be present for many years before vision is affected. The grading of retinopathy has significant implications regarding risks of visual loss, timing of follow-up, and necessity for treatment. In the ETDRS, the cumulative rate of progression from mild non-proliferative retinopathy (mild NPDR) to high-risk proliferative diabetic retinopathy (PDR) was 0.8% at 1 year and 15.5% at 5 years. In contrast, the rate of progression from severe NPDR to high-risk PDR was 14.6% at 1 year and 56% at 5 years. Therefore, eyes with mild NPDR may be safely followed at yearly intervals, whereas eyes with severe NPDR must be followed at least 4 monthly. The commonest cause of visual loss in nonproliferative diabetic retinopathy is macular oedema. This is characterized by the gradual accumulation of fluid and lipid in the macular region of the retina as a result of chronic retinal capillary leakage. Macular oedema typically causes gradual, moderate visual loss which can progress to legal blindness (vision less than 6=60 in both eyes). In addition to classifying levels of retinopathy, the ETDRS used the term `clinically significant macular oedema' (CSME) to describe thickening of the retina (often associated with lipid exudates) involving the centre or near the centre of the macula. Retinal thickening is detected ophthalmoscopically using binocular stereoscopic viewing but may also be detected on stereoscopic photographs. The finding of CSME has significant

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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Figure 21E.1 Natural progression of diabetic retinopathy a. Normal retina (right eye) b. Moderate non-proliferative retinopathy with microaneurisms, haemorrhage and lipid exudate (left eye) c. Moderate non-proliferative retinopathy with visual loss due to macular oedema (left eye) d. Severe non-proliferative retinopathy with widespread IRMA and cotton wool spots (right eye) e. Advanced proliferative retinopathy with visual loss due to vitreous haemorrhage (left eye)

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DIABETIC RETINOPATHY Table 21E.1 Classification of diabetic retinopathy (1) Diabetic retinopathy stage

Clinical features

Minimal NPDR (background) Mild NPDR (background) Moderate NPDR (transitional) Severe NPDR (preproliferative)

Mas only Mas and occasional retinal haemorrhage Mas, more severe retinal haemorrhage, cotton wool spots and hard exudates At least one of the following: Severe Mas and severe haemorrhage in all quadrants Venous beading in at least two quadrants NVE, NVD < 13 disc area NVD > 13 disc area, NVE > 12 disc area with vitreous or preretinal haemorrhage High-risk PDR with traction retinal detachment involving the macula or dense vitreous haemorrhage

PDR High-risk PDR Advanced PDR

NPDR = non-proliferative diabetic retinopathy; PDR = proliferative diabetic retinopathy; Ma = microaneurysm; NVE = neovascularization elsewhere; NVD = neovascularization on or within one disc diameter of the optic disc; IRMA = intraretinal microvascular abnormalities.

implications for treatment which will be discussed in a later section. Proliferative diabetic retinopathy is characterized by the formation of neovascularization (new blood vessels growing forward from the surface of the retina) in response to widespread retinal ischaemia. These new vessels are fragile and prone to bleeding (vitreous or preretinal haemorrhage), causing sudden visual loss which may be severe and prolonged. Continued fibrovascular proliferation into the vitreous may result in traction, retinal detachment and permanent blindness. PREVALENCE AND INCIDENCE Prevalence One of the major limitations of prevalence studies is the inability to establish temporality. Selective survival can be a source of disparity when comparing quantified risk factors between prevalence and incidence studies. (6) Prevalence rates of diabetic retinopathy are available from many studies and are summarised in Table 21E.2 (7 ± 53). The Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) is the only study devoted solely to describing the prevalence, incidence, and risk factors associated with diabetic retinopathy (20, 21). Patients were recruited through their physicians between 1 July 1979 and 30 June 1980. The cohort comprised 1210 Type 1 diabetes patients diagnosed before the age

of 30 and 1370 patients diagnosed after the age of 30. Results from WESDR comprise a major number of papers on the epidemiology of diabetic retinopathy and will be referred to throughout this chapter. Although rates of diabetic retinopathy vary by time and geographic location, nearly everyone with diabetes will develop diabetic retinopathy within 20 years of diagnosis. The prevalence of any diabetic retinopathy in people with diabetes duration less than 5 years varied from 0 in Detroit (10) and 2% in Portuguese (40) to 18% in Italians (31), 22% in Nauruans (16 ± 18), 23% in Africans (28), 32% in non-Hispanic whites in the US (35) and Spanish (43), and 35% in Taiwanese (42). At the time of diabetes diagnosis, prevalence of any diabetic retinopathy varied from less than 1% in Rancho Bernardo, California (38), to 10% in Beaver Dam, Wisconsin (39), 14% in nonHispanic whites in the US and 16% in Mexican Americans (30), and 14% in female Samoans and 17% in male Samoans (51). These differences in retinopathy rates at diabetes identification clearly reflect access and use of health services.

Incidence In comparison with prevalence studies, there are far fewer studies of the incidence of diabetic retinopathy (54 ±69) (Table 21E.3). Also, different grading systems have been used, making it difficult to compare the results across study populations.

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Table 21E.2 Prevalence of diabetic retinopathy in various populations Author (ref), year

Setting

Osuntokun (7), 1969

Nigeria

Kahn (8), 1975

Joslin Clinic

Jarrett (9), 1979

WHO multinational

Frank (10), 1980

Detroit

Mitchell (11, 12), 1980, 1990

Clinic in Newcastle, Australia

Number 758

DM type

DM duration Retinopathy (y) stage

Type 2 diabetes ‡ Type 1 diabetes

5 >10 >15 >20 914 Type 2 diabetes ‡ 16 Type 2 diabetes 0 1±4 5±9 10‡ Type 2 diabetes ‡ Æ10 Type 1 diabetes 11 ± 20 21 ± 40

Klein (20 ±23), 1984, 1987

Wisconsin WESDR

2366

Type 2 diabetes

Varied

Dx < 30y Type 1 diabetes Dx > 30y

Jerneld (24), 1986

Sweden

399

Type 1 diabetes

0 ± 10 11 ± 20 21 ± 30 31‡

Fujimoto (25), 1987

Japanese American men

Kingsley (26), 1988 Orchard (27), 1990 Rolfe (28), 1988

Pittsburgh children's hospital Central Africa

78 696 600

Type 2 diabetes ‡ Varied Type 1 diabetes Type 1 diabetes Varied Type 2 diabetes ‡ Type 1 diabetes

0±6 7 ± 13 14‡

Any

Prevalence (%)

Background Proliferative Background Proliferative Background Proliferative Any Proliferative IRMA Macular oedema Any Proliferative IRMA Macular oedema Any Proliferative IRMA Macular oedema Any Proliferative Any Proliferative Any Proliferative Any Proliferative Any Proliferative Severe

3.6 7.0 11.7 23.1 50.0 7 26 63 Range 2.2 ± 30.0 Range 17.8 ± 75.0 Range 32.4 ± 77.8 0 27 71 49 7 10 35 5 10 28 64 8.2 23 43 60 6 22 37 57 8.4 1.9 20.1 7.1 27.3 18.2 39.1 2.9 2.6 3.7 71 23 16.5 11.1 70.2 14.1 8.8 15.2 19 2 58 10 88 34 100 65 11.5 2.6 57 (6.4

Men without diabetes

Type 2 diabetes

Risk ratio (diabetic=nondiabetic)

31.1 44.7 110.7

138.4 165.7 361.8

4.4 3.7 3.3

Reproduced from (34) by permission.

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Cardiovascular disease deaths per 1000 men per 10 years Number of risk factors None Any one Any two All three

Men without diabetes

Type 2 diabetes

Risk ratio (diabetic=nondiabetic)

6.0 12.2 22.4 47.4

30.7 58.8 90.9 125.2

5.1 4.8 4.1 2.6

Reproduced from (34) by permission.

factor profile. This phenomenon is attributed to `diabetes per se' and may relate to glycation, endothelial damage, or subtle atherogenic lipid fractions. With the current focus on the specific dyslipidaemia of `the metabolic syndrome' (the syndrome of insulin resistance) high triglyceride levels and low HDL-cholesterol levels without any marked elevation of total cholesterol levels are recognized as a marker for cardiovascular disease risk. The concept has emerged of hidden atherogenic lipoprotein such as small dense LDL. There is convincing evidence from studies of predominantly non-diabetic populations that the pattern of an adverse cholesterol to HDL ratio and low levels of HDL-cholesterol and high triglycerides strongly predict cardiovascular disease deaths in diabetic subgroups, as evident in the Prospective Cardiovascular Munster (Procam) Study (35), the Helsinki Heart Study (36), and a retrospective analysis of the Framingham Heart Study (37). Incidental observations on diabetic subjects taking part in intervention trials support the hypothesis that dyslipidaemia is an important determinant of cardiovascular death in diabetic subjects. In the Helsinki Heart Study (38) 135 men with Type 2 diabetes from a cohort of 4081 men in a primary prevention trial used the lipid modifying agent gemfibrozil, for middle-aged men with highnon-HDL-cholesterol (>5:2 mmol=l). Compared with non-diabetic subjects, Type 2 diabetes men had lower HDL-cholesterol and higher triglyceride concentrations, greater body mass index, and more

were hypertensive. The incidence of myocardial infarction and cardiac death was significantly higher among diabetic than non-diabetic participants (7.4% versus 3.3%). Coronary heart disease incidence in the gemfibrozil-treated diabetic men was 3.4% compared to 10.5% in the placebo group ( p ˆ 0:19). Although the comparatively low numbers precluded any possibility of a statistically significant outcome, the dramatic reduction in deaths in the diabetic males (Figure 22.4) was very suggestive. The Scandinavian Simvastatin Survival Study (`4S') was designed to evaluate the effects of cholesterol reduction with simvastatin on mortality in patients with pre-existing coronary artery disease. A total of 4444 patients with serum cholesterol levels 5.5 ± 8.0 mmol=l were randomly assigned to double blind treatment with simvastatin or placebo. Subgroup analysis of those with and without diabetes showed marked reduction in risk of all cause mortality and of major coronary events by the cholesterol-modifying agent (Table 22.4) (39).

A

B

10

5-year incidence of CHD (%)

Table 22.3 Multiple Risk Factor Intervention Trial (34): ageadjusted cardiovascular disease mortality in men with and without diabetes at initial screening. Those with no risk factors, and combinations of risk factors, are shown. Definitions of riskÐ systolic blood pressure >120 mmHg; serum cholesterol >5.2 mmol=l; any cigarette smoking

5

0

p < 0.02

p = NS

Figure 22.4 Helsinki Heart Study: five year incidence of myocardial infarction and death from coronary heart disease (CHD) A: Type 2 diabetes patients (N = 135 stippled bar) Other subjects (N = 3946 open bar) B: Type 2 diabetes patients on placebo (N = 76 hatched bar) Type 2 diabetes patients on gemfibrozil (N = 59 solid bar)

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THE EPIDEMIOLOGY OF DIABETES MELLITUS Table 22.4 Scandinavian Simvastatin Survival Study (39) of subjects with prior myocardial infarction: major coronary events in the subgroup with diabetes (97 on placebo, 105 on Simvastatin) compared with non-diabetics (2126 on placebo, 2116 on Simvastatin) and the relative risk over 5.4 years follow-up Scandinavian Simvastatin Survival Study End Point Category

Percentage with Endpoints On placebo (%) On Simvastatin (%)

Relative risk (95% confidence limits and p values)

Total Mortality Diabetes No diabetes

25 11

14 8

0.57 (0.03±1.08; p ˆ 0:087) 0.71 (0.58±0.87; p ˆ 0:001)

Major coronary events Diabetes No diabetes

45 27

20 19

0.45 (0.27±0.74; p ˆ 0:002) 0.67 (0.61±0.77; p ˆ < 0:0001)

Reproduced from (39) by permission.

Thus there is suggestive evidence for a major role of lipids in causing the excess cardiovascular disease mortality in Type 2 diabetes, and clearly a compelling need to conduct appropriate intervention trials in diabetic subjects. Haffner and colleagues (40) recommend an aggressive approach to the treatment of dyslipidaemia in Type 2 diabetes. They show that Type 2 diabetic patients in Finland without prior myocardial infarction have as high a risk of coronary heart disease mortality as non-diabetic patients with a history of myocardial infarction.

HYPERTENSION (AND ENDOTHELIAL FUNCTION) In the UKPDS, `tight control' of elevated blood pressure levels in Type 2 diabetes, compared with less tight control, reduced deaths related to diabetes by 32% ( p = 0.019). All cause mortality was reduced by 18% but this was not significant ( p = 0.17). The mean blood pressure level at entry was 160=94 mmHg (41). In contrast, the HOPE study evaluated the use of the antihypertensive agent ramipril, (an angiotensin converting enzyme inhibitor) in patients at high risk of cardiovascular disease, but without a specific focus on lowering blood pressure levels. In the subgroup with Type 2 diabetes, mean blood pressure levels at entry were 142=80 mmHg and were not lowered significantly by therapy. But treatment resulted in marked reduction in cardiovascular deaths of 37% ( p = 0.001) and a reduction in total mortality of 24% ( p = 0.004). The

authors suggest that the benefit may be mediated through a direct protective effect on the arterial wall, and by improving endothelial function (42).

MICRO-ALBUMINURIA AS A RISK FACTOR FOR MORTALITY Studies of micro-albuminuria in uncomplicated Type 1 diabetes through to mortality are just not available, although the inference is quite firm that micro-albuminuria leads to an increased risk of macro-albuminuria and hypertension, and there is little doubt that macro-albuminuria is a major risk factor for death especially nephropathy, but also coronary heart disease, in Type 1 diabetes. There has been debate as to whether antihypertensive agents protect against progression of nephropathy merely by their blood pressure lowering properties, or whether angiotensin converting enzyme inhibitors (ACE inhibitors) have specific benefit by an action in lowering increased glomerular capillary pressure to normal (43). Table 22.5 The effect of angiotensin-converting-enzyme inhibition on diabetic nephropathy. Outcome events in patients in Captopril and placebo groups (41) Event

Captopril (n ˆ 207)

Placebo (n ˆ 202)

Death Dialysis or transplantation Death, dialysis or transplantation

8 (4%) 20 (10%) 23 (11%)

14 (6%) 31 (15%) 42 (21%)

Reproduced from (41) by permission.

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DIABETES MORTALITY

In a carefully designed study with definitive outcome, therapy with ACE inhibitors (in this instance Captopril), significantly reduced mortality rate and the combined end point of mortality or renal transplantation or dialysis (44). This benefit was shown to be independent of blood lowering effect per se. ACE inhibitor therapy was associated with a 50% reduction in risk from the combined endpoints (Table 22.5). This outcome was meaningful and independent of the small disparities in baseline blood pressure levels in the two groups ( p ˆ 0:006). GLYCAEMIC CONTROL The issue as to whether small departures from normoglycaemia contribute to the `diabetic factor' that magnifies and accelerates atherosclerotic disease has not yet been resolved. Subjects in the Bedford Study with borderline diabetes (impaired glucose tolerance) had almost the same 10-year mortality from coronary heart disease as frank diabetics (45). A `threshold phenomenon' for the 2 hour plasma glucose levels and subsequent coronary heart disease mortality was demonstrated in the Whitehall Study (plasma glucose level >5:3 mmol=l) and the Paris Prospective Study (plasma glucose level >7:8 mmol=l) (46, 47). The University Group Diabetes Program had specifically addressed this issue in a clinical intervention trial (48). No benefit from close glycaemic control as achieved with variable dose insulin injections was seen, but an unexpectedly high mortality occurred in the tolbutamide treated group. This has been the subject of continuing controversy and disagreement (49). Recent retrospective analysis of 22 studies involving 95 783 persons has shown that non-diabetic elevation of blood glucose levels on univariate analysis is associated with increased cardiovascular morbidity=mortality of modest degree. Compared with a glucose level of 4.2 mmol=l, a fasting plasma glucose of 6.1 mmol=l and a 2 hour level of 7.8 mmol=l have relative risks of 1.33 (1.06±1.67) and 1.58 (1.19±2.10) respectively (50). There is as yet no evidence that moderate hyperglycaemia if modified influences mortality outcomes. Indeed, the United Kingdom Prospective Diabetes Study (51) reports that attempted intensive blood glucose control of established Type 2 diabetes, while reducing substantially the incidence

of microvascular complications, especially retinopathy, had a small and non- significant impact on all cause mortality (6% lower, p ˆ 0:44) and on diabetes-related deaths (10% lower, p ˆ 0:34). Importantly, neither sulphonylurea drugs nor insulin therapy led to any adverse cardiovascular outcomes (51). CONCLUSION In the next decade, mortality studies will provide continuing insights into factors associated with the unacceptably high death rates of diabetic subjects. Careful description of baseline cohorts of persons with diabetes, and the subsequent accurate assessment of true causes of death from multiple source ascertainment are required. The precise nature of `the diabetic factor' that contributes to cardiovascular and renal disease in both Type 1 diabetes and Type 2 diabetes needs urgent elucidation. The changing patterns of mortality that are emerging over time demand an explanation (52). It is to be hoped that current clinical intervention trials in diabetic subjects, long overdue, will not only establish the value of risk factor modification but also will give new and valuable insights into the mechanisms of lethal diabetic vascular complications. REFERENCES 1. West KM. In: KM West (ed.), Epidemiology of Diabetes and its Vascular Lesions. New York, NY, Elsevier Press, 1978: pp. ix and 159± 160. 2. Himsworth HP. Diet and the incidence of diabetes mellitus. Clin Sci (1935); 2: 117± 48. 3. Bild DE, Stevenson JM. Frequency of recording of diabetes on US death certificates: analysis of the 1986 national mortality follow back survey. J Clin Epidemiol (1992); 45: 275± 281. 4. Andresen EM, Lee JAH, Pecloraro RE, Koepsell TD, Hallstorm AP, Siscovick DS. Under-reporting of diabetes on death certificates, King County, Washington. Am J Publ Health (1993); 83: 1021±1024. 5. Fuller JH, Elford J, Goldblatt P, Adelstein AM. Diabetes mortality: new light on an underestimated public health problem. Diabetologia (1983); 24: 336± 337. 6. Waugh NR, Dallas JH, Jung RT, Newton RW. Mortality in a cohort of diabetic patients. Diabetologia (1989); 32: 103± 104.

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7. Christlieb AR, Warram JH, Krolewski AS, Busick EJ, Ganda OP, Asmal AC, Soeldner JS, Bradley RF. Hypertension: the major risk factor in juvenile onset insulin dependent diabetic. Diabetes (1981); 30 (suppl 2): 90 ± 96. 8. Dorman JS, Laporte RE, Kuller LH, Cruickshanks KJ, Orchard TJ, Wagener DK, Becker DJ, Cavender DE, Drash AL. Pittsburgh insulin dependent diabetes mellitus morbidity and mortality study. Mortality results. Diabetes (1984); 33: 271±276. 9. Moss SE, Klein R, Klein BE. Cause specific mortality in a population based study of diabetes. Am J Public Health (1991); 81: 1158± 1162. 10. Portuese E, Orchard T. Mortality in insulin dependent diabetes. In: Diabetes in America, 2nd edn. 1995. NIH Publication 95 ± 1468. Bethesda, MD, National Diabetes Data Group, National Institutes of Health, 1995: pp. 221± 232. 11. White P. Diabetes in Childhood and Adolescence. Philadelphia, PA, Lea and Febiger, 1932: pp. 22 ± 23. 12. Orchard TJ. From diagnosis and classification to complications and therapy: DCCT part II? Diabetes Care (1993); 17: 326± 338. 13. Borch-Johnsen K, Kreiner S, Deckert T. Mortality of Type 1 (insulin dependent) diabetes mellitus in Denmark: a study of relative mortality in 2930 Danish Type 1 diabetic patients diagnosed from 1933 to 1972. Diabetologia (1986); 29: 767± 772. 14. Borch-Johnsen K. The prognosis of insulin dependent diabetes mellitus. An epidemiological approach. Danish Med Bull (1989); 36: 336± 348. 15. Anderson AR, Christianson JS, Anderson JK, Kreiner S, Deckert T. Diabetic nephropathy in Type 1 (insulin dependent) diabetes: an epidemiological study. Diabetologia (1983); 25: 496± 501. 16. Klein R, Moss SE, Klein BE, de Mets DL. Relation of ocular and systemic factors to survival in diabetes (WESDR). Arch Int Med (1989); 149: 266± 272. 17. Moy CS, Laporte RE, Dorman JS, Songer TJ, Orchard TJ, Kuller LH, Becker DJ, Drash AL. Insulin dependent diabetes mellitus mortality. The risk of cigarette smoking. Circ (1990); 82: 37 ± 43. 18. Portuese EI, Kuller L, Becker D, Ellis D, Lloyd CE, Orchard TJ. High mortality from unidentified CVD in IDDM: Time to start screening? Diabetes Res Clin Pract (1995); 30: 223± 231. 19. DERI Mortality Study Group. Major cross-country differences in risk of dying for people with IDDM. Diabetes Care (1991); 14: 49± 54. 20. Diabetes Epidemiology Research International Group. International evaluation of cause specific mortality and IDDM. Diabetes Care (1991); 14: 55± 60. 21. Diabetes Epidemiology Research International (DERI) Study. DERI Mortality Study Group. International analysis of insulin dependent diabetes mellitus mortality: a preventable mortality perspective. Am J Epidemiol (1995); 142: 612± 618.

22. Deckert T, Poulsen JE, Larsen M. Prognosis of diabetes with diabetes onset before the age of 31. Factors influencing the prognosis. Diabetologia (1978); 14: 371± 378. 23. Deckert T, Poulsen JE, Larsen M. The prognosis of insulin dependent diabetes mellitus and the importance of supervision. Acta Med Scand (1979); 624 (suppl): 48 ± 53. 24. Geiss LS, Herman WH, Smith PJ. Mortality in noninsulin-dependent diabetes. In: Diabetes in America, 2nd edn. NIH Publication 95 ± 1468. Bethesda, MD, National Diabetes Data Group, National Institutes of Health, 1995: pp. 233± 258. 25. Tull ES, Roseman JT. Diabetes in African Americans. In: Diabetes in America, 2nd edn. NIH Publication 95 ± 1468. National Diabetes Data Group, National Institutes of Health, 1995: pp. 624± 625. 26. Centres for Disease Control: Diabetes Surveillance 1993. Division of Diabetes Translation CDC 1993. 27. Stern MP, Patterson JK, Mitchell BD, Haffner SM, Hazuda HP. Overweight and mortality in Mexican Americans. Int J Obes (1990); 14: 623± 629. 28. Newman JM, De Stefano F, Valway SE, German RR, Muneta B. Diabetes-associated mortality in Native Americans. Diabetes Care (1993); 16 (suppl 1): 297± 299. 29. Kleinman JC, Donahue RP, Harris MI, Finucane FF, Madans JH, Brock DB. Mortality among diabetics in a national sample. Am J Epidemiol (1988); 128: 389± 401. 30. Moss SE, Klein R, Klein BE. Cause specific mortality in a population based study of diabetes. Am J Public Health (1991); 81: 1158± 1162. 31. Jarrett RJ, Shipley MJ. Type 2 non-insulin dependent diabetes mellitus and cardiovascular disease. Putative association via common antecedents. Further evidence from the Whitehall study. Diabetologia (1988); 31: 727± 740. 32. Morrish NJ, Stevens LK, Fuller JH, Jarrett RJ, Keen H. Risk factors for macrovascular disease in diabetes mellitus: the London follow-up to the WHO multinational study of vascular disease in diabetics. Diabetologia (1991); 34: 590± 594. 33. Uusitupa MIJ, Kiskanen LK, Siitonen O, Voutilainen E, Pyorala K. 10 year cardiovascular mortality relation to risk factors and abnormalities in lipoprotein composition in Type 2 (non-insulin dependent) diabetic and non-diabetic subjects. Diabetologia (1993); 36: 1175±1184. 34. Stamler J, Vaccaro O, Neaton JD, Wentworth D. Diabetes, other risk factors, and 12-yr cardiovascular mortality for men screened in the Multiple Risk Factor Intervention Trial. Diabetes Care (1993); 16: 434± 444. 35. Manninen V, Tenkanen L, Koskinen P, Huttenun JK, Manttari M, Heinonen OP, Frick MH. Joint effects of serum triglyceride and LDL cholesterol

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36.

37. 38.

39.

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42.

43. 44.

and HDL cholesterol concentrations on coronary heart disease risk in the Helsinki Heart Study. Implications for treatment. Circ (1992); 85: 37 ± 45. Assmann G, Schulte H, Triglycerides and atherosclerosis: results from the Prospective Cardiovascular Munster Study. Atheroscler Rev (1991); 22: 51 ± 57. Castelli WP. Epidemiology of triglycerides; a view from Framingham. Am J Cardiol (1992); 70: 3H±9H. Koskinen P, Manttari M, Manninen V, Huttunen JK, Heinonen OP, Frick MH. Coronary heart disease incidence in NIDDM patients in the Helsinki Heart Study. Diabetes Care (1993); 15: 820±825. Kjekshus J, Pedersen TR, for the Scandinavian Simvastatin Survival Study Group. Reducing the risk of coronary events: evidence from Scandinavian simvastatin survival study (4S). Am J Cardiol (1995); 76: 64C ± 68C. Haffner SM, Lehto S, RoÈnnemaa T, Pyorala K, Laalksom M. Mortality from coronary heart disease in subjects with Type 2 diabetes and in non-diabetic subjects with and without prior myocardial infarction. N Engl J Med (1998); 339: 229± 234. UK Prospective Diabetes Study Group. Tight blood pressure control and risk of macrovascular and microvascular complications in Type 2 diabetes: UKPDS 38. BMJ (1998); 317: 703± 713. Heart outcomes prevention evaluation (HOPE) study investigators. Effects of ramipril on cardiovascular and microvascular outcomes in people with diabetes mellitus: results of the HOPE study, and MICROHOPE sub study. Lancet (2000); 355: 252±259. Remuzzi G, Ruggenenti P. Slowing the progression of diabetic nephropathy. N Eng J Med (1993); 329: 1496± 1497. Lewis EJ, Lawrence G, Hunsicker MD, Bain RP, Rohde RD. The effect of angiotensin- converting-

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enzyme inhibition on diabetic nephropathy. N Engl J Med (1993); 329: 1456± 1462. Jarrett J, McCartney P, Keen H. The Bedford Survey: Ten year mortality rates in newly diagnosed diabetics, borderline diabetics and normoglycaemic controls and risk indices for coronary heart disease in borderline diabetics. Diabetologia (1982); 22: 79 ±84. Fuller JH, Shipley MJ, Rose GA Jarrett RJ, Keen H. Coronary-heart-disease risk and impaired glucose tolerance. The Whitehall Study. Lancet (1980); i: 1373± 1376. EschweÁge E, Ducimetiere P, Papoz L, Claude JR, Richard JL. Blood glucose and coronary heart disease. Lancet (1980); ii: 472± 473. The University Group Diabetes Program. A study of the effects of hypoglycemic agents on vascular complications in patients with adult-onset diabetes. I Design, methods and baseline characteristics. II Mortality results. Diabetes (1970); 19 (suppl 2): 747± 830. Fuller JH. Clinical trials in diabetes mellitus. In: JI Mann, K PyoraÈlaÈ, Teuscher A (eds), Diabetes in Epidemiological Perspective. Edinburgh, Churchill Livingstone, 1983: pp. 265± 269. Coutinho M, Wang Y, Geistein HC, Yusuf S. The relationship between glucose and incident cardiovascular events. Diabetes Care (1999); 22: 233± 240. UK Prospective Study Group. Intensive blood glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS33). Lancet (1998); 352: 837± 853. Sasaki A, Kamado K, Uehara M. A changing pattern of causes of death among diabetic patients during a 25 year period in the Osaka District Japan. Diabetes Res Clin Pract (1991); 13: 213± 220.

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Part V

Implications

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23

Economic Costs Thomas J. Songer

University of Pittsburgh, Pittsburgh, PA, USA

INTRODUCTION A common question posed throughout the world today regards the costs associated with diabetes. Cost and other forms of economic data are now important contributors to the decisions made by health departments, health care providers, and health insurers regarding diabetes. Decisionmakers need reliable economic information to equitably determine which health services should be funded (e.g. diabetic care vs the control of communicable disease). They need information for decisions on payment for insulin, syringes, oral hypoglycemic agents, and blood glucose testing supplies, and for decisions on the number of facilities for patients with diabetes and the number of medical personnel trained to treat them. Decision-makers also need information on the relative value for money of health care treatments and programmes to determine the merit of new treatments in diabetes: such as intensive insulin treatment and screening for microalbuminuria. How did we get to this point? Several factors have contributed to the current situation. First, health care is a dynamic process. Medical advances and new therapies require us to adapt and change our approaches to health care. Many new advances, though, are expensive technologies, surgeries, or drugs. Second, health care provision has, in many circumstances, been a politicalÐprocess. In many areas, an oversupply of physicians and a redundancy in facilities (hospitals) exists. Third, the life expectancy of the world's population is increasing. With aging populations comes an increased need for the long-term treatments that surround chronic diseases. These characteristics have all played a role in the current reality that faces many health systems.

1. The costs of health care are rising at a significant rate. Over the last 30 years, expenditures for medical care have risen extensively throughout the world (1, 2, 3). Indeed, health care expenditures have risen much faster than the cost increases reported in other sectors of the economy. 2. There are limited resources available for health care and, often, an inability to obtain more. 3. Health care coverage is often not available to all individuals in a population, and many governments are unable to provide it. 4. There is a large variation in health care use by geographical area and health care setting. This variation exists with no apparent difference in a patient's health. As a result, fundamental changes are taking place in health care systems throughout the world. There is now an expanding focus being placed on the economic aspects of health care. The underlying issue throughout most of the discussions on health care reform and the appropriateness of health care is that of costs. Are we spending health care resources wisely? How much does it cost to provide treatment? What benefits are obtained with that treatment? Which treatments provide benefits and at what cost? With limited resources, health care decision-makers must make choices about the direction to pursue in health care and the intensity with which to pursue it. HEALTH ECONOMICS Health economics is a discipline involved with the study of health care costs, limited resources, and the subsequent choices made regarding health care and health. Health economic studies provide

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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information which can help to guide decisions regarding health care funding, planning, provision, and evaluation. Common economic issues that are examined include: *

*

*

*

*

The costs related to disease or illness, such as the money that we spend to treat patients. These costs involve the expenditures for medical care and the treatment of a disease and its complications (hospital care, physician services, nurse educator services, long-term care, drugs, facilities, and overheads, etc.). The indirect costs associated with disease, such as the impact of disability or premature mortality. These costs relate to lost productivity, an inability to continue working, or from an interference in usual activities. The costs of disease that directly affect the patient. Persons with severe illnesses face several economic decisions. These include the out-of-pocket payments for health care and the sacrifices in time (opportunity costs) made in searching for medical care, employment and insurance. The costs of using resources devoted to a specific disease or treatment inappropriately, or the costs of pursuing inappropriate priorities. The influence of excesses or shortages in the number (supply) of health care providers, the availability (supply) of medical technologies and drugs, and the demand for health care.

There are several reasons why it is important to understand these issues as they apply to diabetes care. First and foremost is that governments increasingly have little money to spend on the health of the public. In this environment, it is important that appropriate levels of resources are directed to diabetes, and that the money available for diabetes is used to its greatest benefit. IDENTIFYING THE COSTS OF DIABETES What are the costs of diabetes? As outlined above, there are a number of different approaches that one can take to examine the costs associated with diabetes. The types of economic costs related to diabetes mellitus include the medical costs of treating persons with diabetes, the indirect costs

related to early disability or early death, the costs that individuals with diabetes face personally when they forgo spending money in one area to help pay for treating diabetes (opportunity costs), the costs of using resources available inappropriately, and the costs of having too few diabetes services (not everyone gets care) or too many diabetes services. Direct Medical Costs Studies of the medical costs of treating diabetes are the most common form of diabetes economic analysis in the literature. Direct medical costs of disease include the expenditure for medical care and treatment of the illness (hospital care, physician services, related laboratory tests, nursing home care, drugs and the cost of daily diabetes management (insulin, syringes, oral hypoglycemic agents, blood testing supplies) among other items. The monetary costs of medical treatment are often easily measured by surveys and studies of medical or billing records. Indirect Morbidity and Mortality Costs Indirect economic costs include the consequences of morbidity, disability and premature mortality resulting from diabetes. These non-medical costs of disease are not easily measured or calculated, but are commonly associated with detrimental changes in activity among persons with diabetes. For instance, the degree to which an individual must stop or decrease the amount of time spent in usual activities. One example is with work. Persons with the complications of diabetes often have to stop working. There is a cost incurred here related to the lost productivity of that individual. These types of costs are also found for persons who die prematurely or for persons with higher rates of absenteeism from work than the general population. Estimates of the monetary costs of these effects are usually based on the human capital approach whereby costs from the illness are valued in terms of lost earnings and production of the patient. This allows for the estimates to be applied as monetary units. However, there is some controversy over the most appropriate method to value losses from premature disability and mortality (4 ±6). Other

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ECONOMIC COSTS

methods exist, such as the willingness-to-pay approach, that value lost opportunities from an estimate that patients provide of the monetary figure that they would be willing to pay to avoid illness, disability, or premature death. The human capital approach has been the only methodology used in the cost of diabetes studies. DIRECT AND INDIRECT COST ESTIMATES Estimates of both direct and indirect costs have been calculated under the framework of cost of illness studies. Dorothy Rice pioneered this estimation approach in the 1960s (7) and further refinements have occurred since. Specific details of these methods are discussed more thoroughly elsewhere (7 ± 11). Table 23.1 outlines the cost of illness estimates reported in the literature for diabetes. Most of the reports are specific to the United States experience, but a number of reports have emerged recently from other countries. Is the economic burden of diabetes significant? The estimates outlined clearly show that the medical costs of diabetes have risen

substantially over the last 25 years. Recent reports suggest that the medical costs of diabetes may be even higher than previously thought. In all of the reports, the largest component of direct medical costs is generally the cost of hospital care related to diabetes (27). The earliest reports on the cost of diabetes were based on work originating from the Statistical Bureau of the Metropolitan Life Insurance Company (SBMLIC) conducted by Paul Entmacher. The SBMLIC estimated the costs of diabetes mellitus over a period of years, and in their reports, the total economic impact of diabetes rose from US $2.6 billion in 1969 to a projected $13.8 billion in 1984 (12). Several reasons have been cited for this increase in costs. They include the inflation of medical care prices, the increased prevalence of diabetes, the increased use of medical services by persons with diabetes and the development of new and expensive treatment technologies for diabetes. In the mid 1980s, cost studies began to examine situations where diabetes was a secondary or tertiary factor listed in health care records. This was due to some concern that estimates based only

Table 23.1 Estimates of the economic cost of diabetes mellitus (cost figures shown are $US bn unless otherwise specified) Study

Year

Country

Total costs

Metropolitan Life (12) Metropolitan Life (12) Metropolitan Life (12) Metropolitan Life (12) National Medical Care Expenditure Survey (12) Policy Analysis (13) Jonsson (14) Platt, Sudovar (15) Miller (16) Laing (17) Metropolitan Life (12) Smeeding, Booton (l7) Carter Center (18) Metropolitan Life (12) Gerard (19) Huse (Type 2 diabetes) (20) Laing,Williams (21) Fox, Jacobs (22) Fox (23) Rubin (24) Gray (Type 1 diabetes) (25) Stern (Type 1 diabetes) (26)

1969 1973 1975 1977 1977

USA USA USA USA USA

2.6 4.0 5.3 6.8 ±

1977 1978 1979 1979 1979± 80 1980 1980 1980 1984 1984 1986 1986± 87 1987 1992 1992 1992 1993

USA Sweden USA USA UK USA USA USA USA England & Wales USA England & Wales USA USA USA England & Wales Israel

10.8 Kr 1317m 15.7 12.4 £144m 9.7 18.9 ± 13.8 £259±603m 19.8 ± 20.4 91.8 ± ± ±

Direct costs 1.0 1.7 2.5 3.4 6.9 3.7 Kr 568m 5.6 7.4 £83.7m 4.8 5.7 7.9 7.4 £239m 11.6 £484m 9.6 45.2 105.2 £96m Shk 104 000 per patient

Indirect costs 1.6 2.3 2.8 3.4 ± 7.1 Kr 749m 10.1 5.0 £60.6m 4.9 10.0 ± 6.3 £20±364m 8.2 ± 10.8 46.6 ± £113m ±

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upon primary diagnosis data would underestimate the true impact of diabetes. Fox and Jacobs (22) and Huse (20) estimated the cost of diabetes in this fashion. They used primary diagnosis data in their report, but also included costs where diabetes was not a primary reason for health care use, disability or death. Not surprisingly, this approach provided higher estimates for the cost of diabetes: approximately $20 billion in 1986 ±7. The latest studies have considered even more health care resources in their estimates, including items such as the costs related to emergency room visits, home health care, and the use of dieticians. Fox-Ray (23) estimated that the cost of diabetes was a staggering $91.8 billion in 1992. Rubin (24) estimated that the health care costs of persons with diabetes were $105.2 billion in 1992. Both findings suggest that the cost of diabetes may be substantially higher than previously thought. The estimates outlined in Table 23.1 have been applied to address many different arguments. These include arguments for changes in reimbursement or health care practices, and the need for adopting new technologies. With incidence-based estimates, it is also possible to have some indication of the savings that might be achieved if the development of diabetes were prevented. Most often, though, cost estimates have been used to highlight the importance of diabetes to health care systems and health care insurers. Diabetes organizations commonly use these data as evidence of the need to devote more resources to diabetes care. Examples of this approach can be found on the World Wide Web. The Juvenile Diabetes Federation in their document, `The Economics of Medical Research' (28), use the cost estimate by Rubin (24), to advance the argument that investing in diabetes research is worthwhile. Is this argument true? Not necessarily. While cost of illness studies can estimate the monetary burden of diabetes, they cannot tell you if you have been spending the money wisely, or if investments in research or clinical practices are worthwhile. These issues are best addressed with another type of economic analysis; a cost-benefit, cost-effectiveness, or cost-utility study. There are limits to the interpretation of all cost estimates. Comparisons between studies are difficult to make because methods and assumptions applied in the studies differ. For example, the 1992 estimate by Fox-Ray is nearly four times larger

than another estimate generated by the same author in 1987. Both studies used different methodologies; the latter including new cost categories and a much higher estimate of the costs of a hospital stay. The framework of the analysis may differ as well. Rubin examined all health care costs among persons with diabetes. This approach may include costs that have little or no connection to diabetes. As most of the expenditures were among persons over age 65 years, it is probable that a great deal of the costs cited had very little to do with diabetes. Thus, the study may over-represent the impact of diabetes. Other cost studies have examined specific topics in diabetes care, rather than the overall cost of diabetes. Examples of these assessments include reports on the cost of diabetes complications, or the cost of diabetes management. Table 23.2 outlines these studies. Opportunity Costs It is not only the health care sector which bears the costs of disease. Individuals with diabetes also face opportunity costs. These are costs that are encountered when a person makes a decision to do one task rather than another. For example, the costs of medicines and equipment sometimes are borne by individuals and their families. Patients may also pay higher life and motor insurance premiums. Persons with diabetes must spend time and money on these items that could otherwise have been spent on something else. They face decisions regarding their lives that other individuals do not. Thus, when we think about the impact of diabetes from the viewpoint of the patient, the costs of diabetes can be quite different, yet still substantial. Specific mechanisms in which diabetes may influence the lives of those with the disease include a diminished access to health care, fewer job opportunities, and, for some, a lower quality of life. There are other areas as well. Persons with diabetes, though, often face decisions over the affordability of diabetes care, and whether it is available or not. They face discrimination in the types of jobs that they can do, and restrictions on or increased payments for health, life, and automobile insurance.

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ECONOMIC COSTS Table 23.2 Topic-specific cost studies in diabetes care Study, year

Diabetes care issue

Findings

Gagliardino, 1993 (29) van Houtum, 1995 (30)

Cost of control relative to the cost of treating complications Cost of lower extremity amputation in diabetes

Apelqvist, 1994 (31)

Cost of treating diabetic foot ulcers

Apelqvist, 1994 (32)

Cost of treatment for foot ulcers

Olsson, 1994 (33)

The impact of excess morbidity in persons with diabetes

Stern, 1994 (26)

Cost of treating Type 1 diabetes and cost of treating complications

Gray, 1995 (25)

Cost of treating Type 1 diabetes and the cost of treating complications Treatment for diabetic end-stage renal disease

Prevention of complications would pay for itself. The average cost per hospital admission for amputation related to diabetes was £10 531 Costs in patients with ulcers that heal primarily are lower than costs in patients with amputations The average weekly cost for topical treatment ranged from £40±385 The excess cost of lost productivity was $7000 per person per year. Excess costs for inpatient care were $800 per person About 70% of the total direct costs of treating complications of Type 1 diabetes relate to treating complications About one-half of the total costs of Type 1 diabetes relate to treating complications Kidney transplants increase renal disease survival and reduce overall treatment costs relative to hemodialysis

Lenisa, 1995 (34)

ESTIMATES OF COSTS FACED BY PATIENTS WITH DIABETES Few reports have examined the economic impact of diabetes in the daily lives of individuals living with the disease. In the United States, however, there is evidence that individuals living with chronic diseases face an increased health care cost burden. Health care in the United States is the most expensive in the world. Access to this expensive system is dictated by the presence of health insurance; both public-based and private. Health insurance coverage varies widely and marked inequities exist in access to health care. About 14% of the general population under age 65 has no health insurance coverage (35). The figure for the diabetes population averages close to 10 ±12% (36, 37). Persons without work or in low-paying jobs are the individuals most likely to be without coverage (38). About 15% of families with Type 1 diabetes children have difficulty in obtaining coverage because of pre-existing illness clauses in the insurance policies (39). As most persons with diabetes use health services more frequently than the general population (40), the out-of-pocket payments for diabetes care can be

extensive. In the United States, where some payment from the patient is often required for services received, out-of-pocket costs to the patient are roughly two times higher for a person with diabetes than for someone without diabetes under age 65 (41). A similar pattern exists among families with Type 1 diabetes children (Table 23.3). Nearly one-third of the families with a child with diabetes surveyed in one study spent more than $1000 of their own money on health care compared to 16% in the families having no diabetic children (39). The burden of out-of-pocket medical expenses appears to be more profound among the lowest income Type 1 diabetes families (Figure 23.1).

Table 23.3 Out-of-pocket health care expenses in families with and without diabetic children Spent per year ($) 0± 499 500± 999 1000± 1499 1500± 1999 More than $2000

Type 1 diabetes families (%)

Control families (%)

32 33 17 5 13

65 19 6 4 6

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THE EPIDEMIOLOGY OF DIABETES MELLITUS Table 23.4 Analyses used in economic evaluation

Figure 23.1 Percentage of income spent on health care by income group (health insurance premiums included)

THE COSTS OF USING RESOURCES UNWISELY

Cost-Benefit Analysis: * examines the costs and benefits of an objective in monetary terms * compares this to an alternative program. * examines if the objective is worthwhile to pursue relative to other programs Cost-Effectiveness Analysis: * examines the monetary cost of an intervention relative to its health outcome * compares this to an alternative (usually an existing standard treatment). * assumes the overall objective is worthwhile, so it examines which intervention better meets the objective Cost-Utility Analysis: * examines the financial cost of a treatment relative to the quality of health produced * compares this to an alternative or a QALY league table * assumes the overall objective is worthwhile, so it examines which intervention better meets the objective

Economic Evaluation A recent and rising practice in diabetes economics has been the use of evaluative studies to examine the economic efficiency of diabetes treatments and programs. Evaluative studies attempt to consider the costs of a treatment or intervention relative to the health outcomes produced. In an era of rising health care costs, these evaluations provide important information that can further decisions about the proper allocation of limited resources. Most often, the costs and outcomes of a new treatment are compared to the costs and outcomes of an existing form of treatment to determine if an efficient and effective use of resources might be gained from adopting the new approach (42). Evaluative designs include cost-benefit, costeffectiveness, and cost-utility analyses. All three designs evaluate the costs and benefits of one intervention or treatment relative to another intervention to determine if resources might be used more efficiently with a change in clinical practice (42 ± 44). Cost-benefit, cost-effectiveness, and cost-utility studies are generally similar in how they estimate costs in an evaluation. Nearly all costs are expressed in monetary terms. The three forms of analysis, though, differ in how they assess the outcomes of a program or treatment (Table 23.4). Cost-benefit studies place a monetary value on the health outcomes of a program. Costeffectiveness studies usually summarize health outcomes in physical units (such as the number of lives saved). Cost-utility studies generally

summarize health outcomes in terms of quality of life gained as well as quantity of life gained. More details on these analyses are available elsewhere (42 ± 44). Differences in the assessment of health outcomes between these three studies can also affect the manner in which these instruments are used in policy decisions. Cost-benefit analyses often seek to determine if an objective (like reducing the impact of diabetes complications) is worthwhile from an economic perspective (4, 45). Since costbenefit studies value outcomes in monetary terms, the results can be compared directly with other programs (both inside and outside of the health sector). It is then, possible to examine whether such a program is worth pursuing and to what degree resources might be committed to it. In contrast, cost-effectiveness and cost-utility analyses already assume that the objective is worth pursuing. The decision here is determining whether one type of intervention or another is better able to meet the intent of the objective. For example, one may find that the objective of reducing blindness from diabetic retinopathy is economically efficient. The task of cost effectiveness and cost-utility analyses, then, is to evaluate which intervention on retinopathy is the most efficient. In general, cost-effectiveness and cost-utility studies are favored over cost-benefit studies in the health-care sector (43). Nearly all of the reports relevant to diabetes have been cost-effectiveness studies.

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Undertaking a cost-effectiveness study can be complex as data are needed from a variety of areas. The appropriate data and endpoints to consider in an evaluation will vary according to the intervention taking place. Most evaluations, however, consider the costs of starting and operating an intervention, the additional direct and indirect costs involved to improve health outcomes, the savings in direct and indirect costs when disorders are prevented or delayed, and the costs associated with any side-effects of the intervention. Other basic principles include stating the perspective in which the evaluation is being conducted, discounting costs that occur in future years, testing the results to consider the impact of uncertainty, and providing a summary measure of the efficiency of an intervention, such as a ratio of costs to effects. These principles are outlined in more detail elsewhere (46), and one recent publication suggests standards that all cost-effectiveness analyses should follow (47). Estimates from Evaluation Studies Economic studies of various diabetes interventions have been gaining in popularity in recent years. Evaluation studies differ substantially from cost of diabetes studies. They usually consider the costs of a diabetes program or treatment relative to the benefits obtained. In this design, comparisons are often made with an existing standard in an attempt to prioritize the direction of future resources and treatments. One current example is the evaluation of screening programs for determining the appropriate detection method and treatment setting for identifying diabetic retinopathy in the population. The first studies in the literature dealing with the economic benefits of new treatments primarily examined diabetes education programs. Kaplan and Davis (48) critically discuss a large portion of this literature in their benchmark publication. They point out that many of the early studies neglected significant issues, such as accounting for the costs of starting and operating the education program. Most focused instead on the benefits of the program, and thus, the results give an unbalanced perspective. The majority of studies also did not include control groups for comparative purposes in their evaluations. Kaplan and Davis also point out the danger of evaluating

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programs simply on the basis of a reduction of medical costs since health care costs can be influenced by a variety of factors, including simple changes in how services are paid for. Many of the reports examined by Kaplan and Davis were completed in the 1970s and early 1980s. Since that time, there has been an explosion in the number of reports that address economic issues in diabetes from an evaluation perspective. Table 23.5 outlines the majority of these studies. The evaluations conducted center around screening or treatment programs for diabetes complications (e.g. retinopathy, renal disease, foot ulcers) or adverse outcomes of diabetic pregnancies. Overall the findings suggest that screening for and treating diabetes retinopathy is extremely costeffective. Javitt (55) found that it saved $62± 109 million for an annual incidence cohort of patients with Type 1 diabetes (when compared to not screening). A further report found that additional savings of $9500 may occur with each new Type 1 diabetes person enrolled in a screening program (over the number already being screened)(57). While it appears that some type of screening for diabetic retinopathy is better than no screening at all, there is debate over its level of efficiency in different settings and among different populations. Dasbach (56), for example, found that the costs of screening and subsequent treatment were recovered for persons using insulin (both young and old), but not for persons diagnosed with diabetes at a later age and not on insulin. Lairson (61), Griffith (68), and Joannu (83) reported that screening with retinal photographs can be an efficient alternative to a standard ophthalmologist's examination in settings where ophthalmologists are scarce. The underlying message of these reports is that appropriate screening strategies (and appropriate interventions in general) will vary. The most efficient means for screening can depend upon the population being screened, the training level of the screeners, the availability of equipment, and the goals of the program relative to the resources available to carry it out. A similar message exists with regards to screening and treatment for microalbuminuria. The general findings of three reports suggest that screening for microalbuminuria and treatment with angiotensin converting enzyme inhibitors (64, 79) or antihypertensives (66) would be beneficial and costefficient. The strength of the data that underlie

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Table 23.5 Evaluative studies of diabetes-related issues in the literature Study

Issue examined

Results

Deckert, 1978 (50) England, 1981 (50)

Cost-benefit analysis of outpatient care in a diabetes clinic Cost-effectiveness of a vaccine to prevent Type 1 diabetes

Weiner, 1986 (51)

Screening strategies for gestational diabetes

Kaplan, 1987 (52)

Cost-utility of diet and exercise intervention in Type 2 diabetes patients

Javitt, 1989 (53) Allan, 1990 (54) Javitt, 1990 (55)

Cost-effectiveness of treatment and control of diabetic retinopathy in Type 1 diabetes patients Costs and effects of glucose self-monitoring in Type 2 diabetes patients Efficiency of screening strategies for detecting diabetic retinopathy in Type 1 diabetes patients

Dasbach, 1991 (56)

Screening and treatment strategies for diabetic retinopathy

Javitt, 1991 (57)

Efficiency of current screening conditions and those at higher levels of compliance

Sculpher, 1991 (58) de Weerdt, 1991 (59)

Screening strategies for referring cases of diabetic retinopathy Costs and effects of a diabetes education program for insulin-treated patients

Fendrick, 1992 (66)

Cost-effectiveness of screening and treatment of diabetic retinopathy among Type 1 diabetes patients in Sweden relative to no screening

Lairson, 1992 (61)

Screening strategies for referring cases of diabetic retinopathy

Scheffler, 1992 (62) Sculpher, 1992 (63)

Cost-effectiveness of a diabetes pregnancy intervention program Additional screening strategies for referring true cases of diabetic retinopathy

Siegel, 1992 (64)

Screening strategies for early renal disease and treatment with ACE inhibitors in Type 1 diabetes patients

Bentkover, 1993 (65)

Cost-effectiveness of treating non-healing foot ulcers with platelet releasate in a wound care clinic

Borch-Johnsen, 1993 (66)

Cost-benefit of screening and treatment for microalbuminurian Type 1 diabetes patients

Elixhauser, 1993 (67)

Preconception care for diabetic women relative to usual prenatal care

Outpatient care in a Danish diabetes clinic involved little cost ($10 000) and saved $100 000 over 40 years Vaccinating all children at age 3 is preferable to vaccinating only those at high risk. A 50% effective vaccine would save $30 million yearly in direct costs The number of oral glucose tolerance tests required and the cost of identifying gestational diabetes is reduced with 2 hour screening test compared to a 1 hour test The cost-utility of the program was $10 870 per well-year gained; a figure comparable to other advocated health care programs Costs of screening and panretinal photocoagulation per person-year of sight saved = $966 Blood glucose monitoring was no more effective than urine testing, but 8±12 times more expensive Screening for and treating patients with retinopathy realizes a cost savings under many different types of screening programs, ranging from $62±109 million and saving 71 000±85 000 sight years Costs for screening are recovered by the avoided costs of blindness in insulin-taking persons, but not in non-insulin-taking persons At current levels of screening, $101 million and 47 000 sight years are saved in an incidence cohort. Added savings of $9500 occur with each new person screened The efficiency of screening varies by the person doing the screening and the site where it is performed No significant effect of education on metabolic control, use of health services, drug costs, or indirect costs was observed between an experimental and control group. Screening for and treating patients with retinopathy may realize a cost savings of 22± 37 million SEK and 2300±3200 sight years saved depending upon patient compliance to screening recommendations In a government health care setting, screening with retinal photographs (with dilated pupils) is more costeffective than ophthalmoscopy Over $5 in hospital charges was saved for every $1 spent in this preconception and early pregnancy program, Tradeoffs exist such that to increase sensitivity, you have to adopt tests with lower specificities and=or higher costs per case detected Screening for and treatment of microalbuminuria with ACE inhibitors appears to be cost-effective. This result, though, depends upon the drug effectiveness, screening costs, and the cost of end-stage renal diseases Clinic and platelet releasate treatment was more costeffective ($22 500 per healed person) than clinic and saline solution treatment ($36 000 per healed person) Screening for microalbuminuria and treatment with antihypertensive drugs would pay for itself if the rate of increase in albuminuria was reduced by 8±10% a year Intensive medical care before conception appears to result in costs savings from averted complications compared with prenatal care only

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Table 23.5 (continued) Study

Issue examined

Results

Griffith, 1993 (68)

Screening for retinopathy in a primary care setting

Levy-Marchal, 1993 (69) Simell, 1993 (76)

Examination of screening strategies to identify early cases of Type 1 diabetes in the population

Haardt, 1994 (71)

Cost-benefit analysis of implantable insulin pumps compared to multiple injections

Javitt, 1994 (72) Le Floch, 1994 (73)

Savings to the federal budget from screening and treatment for eye disease in Type 2 diabetes patients Cost-effectiveness of screening for microalbuminuria

Starostina, 1994 (74)

Costs and effects of glucose self-monitoring strategies (urine, blood, none) in Russia

Woolridge, 1994 (78)

Cost to a government insurer of paying for therapeutic shoes for patients with foot problems

Collins, 1995 (76)

Savings in prescription costs from a weight reduction program

Eckman, 1995 (77)

Cost-effectiveness of treatments in patients with foot infection and suspected osteomyelitis

Franz, 1995 (78) Kiberd, 1995 (79)

Cost-effectiveness of practice guidelines nutrition care in Type 2 diabetes patients Cost-utility of screening and treatment for diabetic renal disease in Type 1 diabetes patients

Steindel, 1995 (80)

Cost and effects of insulin pump treatment in poorly controlled adolescents

DCCT Research Group, 1996 (81) Javitt, 1996 (82)

Cost-effectiveness of intensive insulin treatment relative to standard treatments

Joannou, 1996 (83)

Screening for retinopathy in a diabetes clinic

Stern, 1996 (84)

Direct costs of intensive insulin treatment relative to standard treatments in Type 1 diabetes patients

Screening by ophthalmoscope and fundal photography both had high sensitivities for referring patients. Direct ophthalmoscopy was less expensive than retinal photos. Cost-benefit ratios (the cost of screening relative to the ability to accurately identify subjects) vary by the population groups examined and the screening test Direct and indirect costs were reduced among patients with shorter lengths of stay (9 days) compared to those with longer stays (23 days). No difference in metabolic control was observed between the groups up to 2 years The implantable insulin pump was more effective in metabolic control over 6 months than multiple injection therapy, but direct costs were 3 times higher Screening and treatment for diabetic eye disease saves $248 million to the federal budget. If all patients are screened, could save up to $472 million The cost-effectiveness ratio of screening with dipsticks and lab assay for identified positives relative to screening with lab assays alone was £6600 per QALY Glucose control was improved and DKA events reduced among subjects in urine or blood monitoring relative to no Both methods were equally effective, but the cost of urine testing strips was markedly lower No difference in future payments for health services up to 1 year was observed between patients with shoes paid for by insurance and patients who bought their shoes Weight reduction was maintained over a 1 year period after intervention. Prescription drug costs were reduced by $442 per person over this time. Several strategies were examined for Type 2 diabetes patients. In general, those involving long courses of antibiotic therapy were preferable to those involving amputation Nutrition interventions that follow practice guidelines can improve metabolic control at a reasonable cost Screening for microalbuminuria and treatment with ACE inhibitors is cost-effective relative to screening and treatment for hypertension and macro-proteinuria if certain conditions are satisfied, including screening costs and accuracy, drug costs, and renal disease costs Insulin pump treatment did not change metabolic control over one year, but did reduce hospital stays and direct costs compared with treatment in the year before Intensive insulin therapy represents a good monetary value. The incremental cost per year of life gained with intensive treatment was $30 400 The cost of detecting and treating diabetic eye disease is $3190 per QALY saved; $1996 for those with Type 1 diabetes, and $3530 for patients with Type 2 diabetes Screening with 60 degree retinal photography was more effective than with 45 degree photos. Cost was $6 per patient screened, $37 for each patient referred Over 35 years, the cost of intensive treatment is about $19 000 higher than the cost of standard treatment. No indirect costs of premature mortality are included

The costs and effects of shorter hospital stays among children diagnosed with Type 1 diabetes

Cost-effectiveness of screening and treatment for diabetic eye disease relative to existing disease

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these reports, though, is not at the same level as that for retinopathy. Several questions remain regarding the level of `effectiveness' of ACE inhibitors in a microalbuminuric population, the degree of compliance to pharmaceutical treatments, and the cost of screening in the diabetic population. As Kiberd (79) points out, the cost-effectiveness of screening for microalbuminuria can vary tremendously by the values assigned to each of these issues. The cost-effectiveness of treatment for diabetic foot ulcers remains muddled. A paper by Eckman et al. (77) examined treatment strategies for foot ulcers with suspected osteomyelitis. The underlying message emerging is that treatment with a long course of antibiotics is more cost-efficient in most situations than amputation. The authors, however, examined several treatment alternatives in their analysis, and as such, one can only draw general conclusions from the work rather than specific recommendations. Herein lies the crux of most economic analyses. Evaluations are most useful when they elicit specific recommendations regarding the pursuit of a treatment, technology, or program. Such recommendations can be readily put to use by decision-makers in health care. Clear recommendations, though, are not apparent for many of the issues pertaining to diabetes care. It is equally important that proper interpretations are applied to the existing reports. Two reports have examined the issue of intensive insulin treatment among persons with Type 1 diabetes. Stern and Levy (84) conducted a cost analysis and found that, over 35 years, the cost of intensive treatment was greater than the cost of standard forms of treatment, even after accounting for the greater costs of treating complications in the standard treatment group. Their report, however, did not consider indirect costs or the relative benefit of improved health associated with intensive treatment. Similarly, the DCCT Research Group (81) found that intensive treatment was more costly than standard treatment; the cost per year of life gained with intensive treatment was $30 400. They concluded, however, that intensive insulin therapy represents a good monetary value. Are services that reduce costs or save money the only services worth pursuing? Many individuals would answer this second question in the affirmative, but such a response would be shortsighted. For it neglects the clear health benefits of many

interventions that do not save money. Diabetes issues should be viewed in a balanced perspective of costs relative to the improvement in health obtained. When is an intervention cost-effective? The answer here depends upon how one values life or health, but indications from the economic literature suggest that interventions costing $20 000 per year of life gained are cost-effective, while those costing more than $100 000 per life year gained are not. OUTCOMES RESEARCH An understanding of the appropriate use of health care may also be gathered from health outcomes studies. At present, large variations in health care interventions (and costs) exist by area (85±87). As there are also no apparent differences in health outcomes, it would appear that either there is inappropriate care or inappropriate priorities are being pursued in some areas. The general aim in formal outcomes studies is to identify the practices and results of medical care interventions over large populations, record the outcomes related to specific practices, and suggest modifications to clinical practice on the basis of these observations (91). There are numerous informal studies that address health outcomes associated with diabetes. Reports of international differences in outcomes, in particular, provide clues towards identifying areas that may have unsuitable care or misplaced priorities. The Diabetes Epidemiology Research International study, for example, illustrated significant differences in mortality among Type 1 diabetes patients in four countries (89). Death rates were highest in Japan, followed by Allegheny County, PA, Finland, and Israel. Reports also exist that describe situations where persons with diabetes face barriers to care or limits (economic and other) on their access to health services. For example, in the United States, persons with Type 1 diabetes who do not have health insurance visit physicians (90) and test their blood sugars (39) less frequently than those who have insurance. Many researchers believe that inadequate attention to routine diabetes management will lead to poor subsequent health, but there is limited evidence, at this time, to prove it. Recent data from the follow-up study to the DCCT, though, indicate that persons without insurance

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coverage have worse levels of glycemic control than those with insurance (91). Thus, the link between access to care and poor health outcomes in diabetes may be legitimate. Formal outcomes studies specific to diabetes are underway (92). The Medical Outcomes Study (MOS) (93) examined the health outcomes of 170 patients with Type 2 diabetes over a 2-year period of time. Overall, no real differences in health outcomes were observed between patients enrolled in health maintenance organizations (HMOs), independent practice associations (IPAs), and traditional fee for service plans. Foot ulcer and foot infection outcomes, though, were better among patients treated by endocrinologists than among patients treated by family or general practitioners. The Type II Diabetes Patient Outcome Research Team (PORT) Study (94) is currently following the medical care and outcome experience of 4000 patients in the United States. Preliminary findings from this study suggest little difference in outcomes (metabolic control) by type of physician (specialist vs. generalist). The Supply of and Demand for Diabetes Care Costs are also involved when there are too few or too many resources in diabetes care. The classic issues in economics are the influences of supply and demand on prices. Although the health care setting is not entirely similar to the business world, several scenarios exist where supply and demand are of concern for their influence on the cost of health care. Excesses or shortages in the number (supply) of health care providers, the availability (supply) of medical technologies and drugs, and the demand for health care may affect the costs of disease or treatments for a disease. An understanding of the demand for and supply of diabetes care can help to explain current cost structures and diabetes care behaviors. An example of too little resources is the finding that insulin is not available to people who need it in some areas of the world. The cost involved here is the increased morbidity and mortality observed among diabetic patients in these areas, because they cannot get care when they need it. Unnecessary costs are involved when too many resources or providers are available. Physicians,

for example, may recommend that a patient return for another visit as their salaries or income are dependent upon it. The hazard in this situation is that of unnecessary treatment or an inefficient use of resources. When too few resources are available, there are costs related to the lack of treatment of disease. When insulin is not available to the patients who need it, the cost is expressed in terms of poor health outcomes; severe hyperglycemia or death (95). The implementation of diabetes treatments, programs, or initiatives (such as the St Vincent Declaration) will also have an associated cost if too few skilled specialists are available to provide intensive insulin treatment as practiced in the DCCT or screening and treatment for diabetic eye disease (96) and other complications. The cost in this scenario is a continued high rate of complications in the diabetes population (97).

CONCLUSIONS An understanding of the economic issues that both patients and society face in treating diabetes is vitally important. Health care choices are a reality of life throughout the world. Health care payers and providers must make decisions that affect not just diabetes, but other non communicable and communicable diseases. Today, many decision-makers are seeking to address and contain health care costs. Most of the changes taking place in health systems focus on cost containment. There are several mechanisms by which one can reduce costs, such as limiting access to care, shifting services from inpatient to outpatient settings, reducing the frequency of health care use, reducing the duration of the patient encounter, and reducing the cost of the service (perhaps with more use of allied health professionals). Each of these actions has an associated cost. The above presentation has briefly introduced the different ways in which the costs of diabetes can be viewed. Yet, given the prevailing mood for cost control, our understanding of the costs of diabetes is limited in many areas. Nearly all aspects of medicine must now address the issue of cost, and the evaluation of most new treatments or technologies commonly includes assessments of costs relative to the benefits obtained. We need

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more economic assessments pertaining to diabetes to address these issues. If not, the danger is that the zeal to control costs may overlook the subtleties in improving the health of persons with diabetes. There are clear health benefits to many interventions that do not save money. We must be concerned that diabetes issues are viewed in the balanced perspective of costs relative to the improvement in health of diabetes patients obtained, rather than the unbalanced perspective that may accompany an emphasis on costs.

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71. Haardt MJ, Dorange C, Selam JL, Mace B, Slama G, Ramaniche ML, Bethoux JP, Bruzo F. A costbenefit comparison of intensive diabetes management with implantable pumps versus multiple subcutaneous injections in patients with Type I diabetes. Diabetes Care (1994); 17(8): 847± 851. 72. Javitt JC, Ferris FL, Aiello LP, Canner JK, Chiang YP, Greenfield S. Preventive eye care in people with diabetes is cost-saving to the federal government; implications for health care-reform. Diabetes Care (1994); 17(8): 909± 917. 73. Le Floch JP et al. Cost-effectiveness of screening for microalbuminuria using immunochemical dipstick tests or laboratory assays in diabetic patients. Diabetic Med (1994); 11: 349± 356. 74. Starostina EG, Antsiferov M, Galstyan GR, Trautner Ch, Jorgens V, Bott U, Muhlhauser I, Berger M, Dedov II. Effectiveness and cost-benefit analysis of intensive treatment and teaching programmes for Type I diabetes mellitus in Moscow Ð blood glucose versus urine glucose self-monitoring. Diabetologia (1994); 37: 170 176. 75. Wooldridge J, Moreno L. Evaluation of the costs to Medicare of covering therapeutic shoes for diabetic patients. Diabetes Care (1994); 17(6): 541±547. 76. Collins RW, Anderson JW. Medication cost savings associated with weight loss for obese non-insulindependent diabetic men and women. Preventive Med (1995); 24: 369± 374. 77. Eckman MH, Greenfield S, Mackey WC, Wong JB, Kaplan S, Sullivan L, Dukes K, Pauker SG. Foot infections in diabetic patients: decision and costeffectiveness analyses. J Am Med Assoc (1995); 273(9): 712± 7202. 78. Franz MJ, Splett PL, Monk A, Barry B, McClain K, Weaver T, Upham P, Bergenstal R, Mazze RS. Cost-effectiveness of medical nutrition therapy provided by dieticians for persons with noninsulin-dependent diabetes mellitus. J Am Diet Assoc (1995); 95: 1018± 1024. 79. Kiberd BA, Jindal KK. Screening to prevent renal failure in insulin dependent diabetic patients; an economic evaluation. Brit Med J (1995); 311: 1595±1599. 80. Steindel BS et al. Continuous subcutaneous insulin infusion (CSII) in children and adolescents with chronic poorly controlled type I diabetes mellitus. Diabetes Res Clin Pract (1995); 27: 199±204. 81. The DCCT Research Group. Lifetime benefits and costs of intensive therapy as practiced in the Diabetes Control and Complications Trial: an economic evaluation. J Am Med Assoc (In Press). 82. Javitt JC, Aiello LP. Cost-effectiveness of detecting and treating diabetic retinopathy. Ann Intern Med (1996); 124: 164± 169. 83. Joannou J, Kalk WJ, Mahomed I, Ntsepo S, Berzin M, Joffe BI, Raal FJ, Sachs E, van der Merwe MT, Wing JR. Screening for diabetic retinopathy in

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Diabetes Field Surveys: Theory and Practical Aspects Gary K. Dowse

Communicable Disease Control Branch, Health Department of Western Australia

INTRODUCTION As the contents of this volume attest, our knowledge of the epidemiology of diabetes has expanded enormously in the two decades since the first book dedicated to this subject was published (1, 2). This has been brought about, in large part, through the contrasting use of two major study variants: the establishment of registers for insulin-dependent diabetes mellitus (Type 1 diabetes) (3, 4), and the widespread use of field survey methodology for the study of non-insulin-dependent diabetes mellitus (Type 2 diabetes) (5, 6). The relatively low prevalence of Type 1 diabetes, coupled with a clearly defined clinical presentation and the imperative of early diagnosis and treatment for survival, mean that it is ideally suited to study by the establishment of incidence registers. This is most applicable in countries which have high ascertainment of cases by virtue of a moderately sophisticated standard of medical practice which is uniformly accessible to the population. Having defined cases through population-based registers, case-control methods are then often utilized for specific aetiologic inquiries in Type 1 diabetes (7, 8). By contrast, Type 2 diabetes is defined in terms of blood glucose concentrations, and a significant proportion of all cases are asymptomatic, and certainly undiagnosed, for some years prior to clinical recognition of the disease (9). In both developed and developing countries, as many as 50% or more of all cases with Type 2 diabetes may be unknown (10 ±12). Moreover, these undiagnosed cases are by no means benign: many individuals will be found to have occult microvascular and macrovascular disease at this

stage (9, 13 ±14). This is not surprising, given that the World Health Organization diagnostic criteria for diabetes were defined on the basis of glucose concentrations at which characteristic microvascular complications of diabetes had been observed to occur (15 ±17, 52, 53). The Need for Surveys Because of the existence of pools of both clinically mild untreated cases and undiagnosed cases, irrespective of the population, studies of Type 2 diabetes and its complications that rely on routine data sources and clinic attenders will be subject to considerable bias. For this reason, population-based surveys are necessary to properly define the spectrum of Type 2 diabetes (15, 18). Such surveys may be prompted by recognition of the growing importance of diabetes as a cause of ill-health, in both social and economic terms, and provide information of use for: * * *

public health planning purposes aetiologic inquiry facilitating appropriate health education and= or referral for treatment of individuals found to have disease or elevated risk factor levels

The need for field surveys is particularly important in developing countries where Type 2 diabetes is becoming increasingly common, and `routine' sources of information, such as hospitalisation and mortality data, are either not available or at best incomplete (5, 6, 18). Indeed, surveys performed in the developing world over the years since standardized criteria for diagnosis of diabetes

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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were promulgated have highlighted the emergence of virtual epidemics of Type 2 diabetes as a significant global public health problem (6, 15, 19). Measurement procedures which are commonly used in epidemiological field surveys of Type 2 diabetes and cardiovascular disease risk factors have been published previously (18, 20 ± 23) and this information will not be repeated here. Similarly, specific descriptions of how to perform field surveys of diabetes and cardiovascular disease whereby up to 120 ±150 subjects can be surveyed in a morning, with 2 hour glucose tolerance tests, questionnaires, anthropometry, blood pressure measurement, electrocardiograms and complications assessments, can be found elsewhere (18, 20). Therefore, this chapter attempts to cover different ground, by providing a theoretical and practical framework for planning and performing a field survey to determine the prevalence of diabetes, and of associated lifestyle risk factors and conditions, and complications. The emphasis will be on suggestions of particular relevance to surveys in developing countries and remote locations, although the methods have wide applicability. It is axiomatic that investigators planning studies should carefully consider the suggestions in this chapter in the context of their own resources and environment, and their objectives. Alderson (24) instructively defined an epidemiologic survey as: `a special inquiry which collects planned information from individuals (usually a sample) about their history, habits, knowledge, attitudes or behaviour. In addition to asking questions, the respondents may be examined or various investigations carried out'. Researchers interested in a more comprehensive description of general survey methods should consult Abramson (25). PLANNING AND PREPARATION FOR A SURVEY Investigators planning a survey should follow a well-defined set of principles, summarized in Table 24.1, and discussed below. While many of these are common sense, even the most experienced researchers could benefit from checking their progress against such a list. It should also be pointed out that while there is some logical sequence to the steps, in reality many of these will be carried out concurrently.

Table 24.1 Summary check-list of steps in planning and performing a survey Planning and Preparation 1. Defining the study objectives 2. Choice of research method 3. Selection of study population and sampling method 4. Sample size determination 5. Choice of study variables and measurement instruments 6. Design of questionnaires 7. Preparation of survey manual, data record forms and other documentation 8. Planning analyses and data presentation 9. Funding applications 10. Ethics approval Organization and Conduct 1. Logistics: dates, staff, survey sites, equipment and supplies 2. Pre-testing, pilot-testing and staff training 3. Census of survey population 4. Promotion and maximizing response 5. Supervision and quality control After the Survey 1. Public relations and follow-up 2. Packing, transportation and storage of equipment and specimens 3. Safe-keeping of survey forms and other documentation 4. Data processing and reporting

Defining the Study Objectives The initial steps in planning any survey (or other form of epidemiological study) are to formulate the research question(s) to be addressed and to define the objectives of the study. This may involve preliminary discussions with peers, potential collaborators, and experts in the field, and critical review of the literature and any available routine data. These steps should help to define important gaps in knowledge so that worthwhile research questions can be formulated. Objectives should be clearly and concisely stated in writing, and should be achievable (20). For example: `To define the relationship between physical inactivity and impaired glucose tolerance (IGT) in population x.' The stated objectives will be critical in determining the type of study which is performed, including the resources (funds, staff, facilities, instruments) and time required to complete the work. Objectives may need to be recast if subsequent steps in planning suggest that they are not achievable.

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Choice of Research Method The nature of the objectives will in large part determine the study design. The simplest observational study is the cross-sectional population survey, the subject of this chapter. These are also known as prevalence studies, and are by far the most commonly performed type of epidemiological study of Type 2 diabetes (5, 6, 10±14). As outlined elsewhere, depending on the study hypotheses, additional elements can be added to the cross-sectional design, including rural±urban comparisons, contrasting the experience of migrant groups with those who have not migrated, and serial surveys to monitor trends in prevalence (18, 26±29). If the objectives include the desire to define the incidence and natural history of disease, and to relate baseline risk factors to incidence of disease and complications, then an initial cross-sectional survey population can form a cohort for a prospective or longitudinal study. Although not essential, it is preferable to have planned to perform a longitudinal study from the beginning, as this will influence many aspects of the design of the baseline survey, and it will be possible to put in place procedures for surveillance of the study population. The latter will allow investigators to more readily trace subjects at the time of follow-up studies, and provide important information on patterns and determinants of morbidity and mortality. Longitudinal studies in several populations, including Britons, Pima Indians, Nauruans and Swedes have contributed much to our current understanding of Type 2 diabetes (30 ± 33). Of course, `survey methods' and the general principles involved in their planning and conduct also contribute to the other major forms of epidemiological study, although these are not `surveys', per se. For example, case-control methodology has been employed in studies of Type 2 diabetes in Hispanic and non-Hispanic Americans (34) and experimental studies have suggested that diet and exercise intervention may prevent Type 2 diabetes (35). Selection of Study Population and Sampling Method The study population is usually defined on the basis of characteristics such as geographic loca-

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tion, age-range, sex and ethnicity. The choice of the population is determined by the study objectives, available resources, likelihood of cooperation and stability of the population, and logistic factors including topography and ease of transport. Having defined the population for study, a sample is usually selected in some way so as to be representative of that population. The ideal of a simple random sample, whereby all members of the target population are eligible for inclusion, is rarely possible. This requires the existence of a complete sampling frame, such as an electoral roll, and considerable resources, given the likely wide geographic spread of subjects. National estimates of diabetes prevalence in the USA (11) and Australia (36) have been based on randomization of individuals, but the former involved a complex system of stratified sampling, and the latter was limited to residents of large cities and did not use glucose tolerance tests (18). Many surveys of Type 2 diabetes have been performed in whole community samples, with all persons within a defined geographic area who meet certain other criteria, including age, being eligible. Boundaries may reflect local administrative areas (12), whole villages (29), or areas defined by the investigators to suit logistic and sample size considerations, and demarcated by streets, rivers or other landmarks (37). The small Pacific island nation of Nauru represents a unique case, with the entire population of adult ethnic Nauruans being eligible for study (32, 38). In the more usual case, community samples are chosen on the basis of logistic factors (e.g. accessibility and appropriate facilities for establishing a temporary survey site), because they are judged to be typical of the background population, and because their characteristics are compatible with the a priori objectives of the study. For example, communities may be selected because their lifestyle is thought to be typically urban or rural (26 ± 29). Where such community samples are studied care must be taken in extrapolating results to the wider population. A case in point is the genetically diverse population of Papua New Guinea, where the prevalence of Type 2 diabetes can vary widely between nearby villages, even where lifestyle appears similar (29). In some circumstances, it might be feasible to use random cluster sampling, whereby the advantages of working in defined communities or areas are retained, but an attempt is made to choose a

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

study sample more representative of the target population. This involves sampling from many geographical clusters of population which each have an equal chance of being selected: clusters will most usually be defined by existing administrative boundaries, but may be created specifically for the survey, for instance, using street boundaries. If the population size varies between clusters, then sampling should be with `probability proportional to size' (39). The larger the number of clusters, the more representative the sample is likely to be of the background population. A national survey of Type 2 diabetes prevalence in Mauritius utilized a two-stage random cluster sampling scheme in which 10 clusters of around 500 ±600 adults were selected (12). Additional sampling strategies include stratification, whereby subgroups (such as an ethnic group or age-group) may be deliberately over- or undersampled, systematic sampling, where, for example, every nth street, household or individual is selected, and multi-stage sampling, whereby a sequence of any of the previously described methods is followed to define the eventual sample (21, 24, 39). However, particularly in unsophisticated populations, response is likely to be optimized where all adult members of a community participate, rather than by singling out individuals from different households and neighbourhoods.

the need for greater precision of estimates and power of a study to find a result, sample size requirements can rise dramatically (39). Of course, surveys of Type 2 diabetes commonly measure many variables, besides the prevalence of the defining condition. These include continuous variables such as blood glucose and cholesterol concentrations, blood pressure, urinary albumin excretion, and body mass index, and a range of categorical variables, including gender, cigarette smoking, ethnicity, presence of retinopathy, hypertension and other conditions, and so on. In turn, many research questions can potentially be studied. It is neither practical nor desirable to make sample size calculations for all possible analytic scenarios. Investigators must decide on their primary objectives, and focus on these in determining the optimal sample size. While available resources and logistic factors must also be considered in planning the size of a study, and the data to estimate sample size may be limited or inaccurate, there are no excuses for failure to consider whether the sample size in any study is sufficient to give useful information. Funding agencies are usually forthright in requiring demonstration that sample size is adequate.

Sample Size Determination

Variables to be measured in a survey must be chosen in relation to the objectives of the study and taking consideration of the likely form of analysis. In general, investigators should resist the temptation to measure too much Ðthis tendency is a particular problem in collaborative studies, where participating researchers may have different interests. This impasse can be overcome, at least to some extent, by limiting measurements of secondary importance, and those that are complex or time-consuming, to subgroups of the main study population. Variables in typical diabetes surveys will be selected from several categories including demographic characteristics (e.g. age, sex, ethnicity, place of birth, socio-economic status indicators), medical and family history indicators (e.g. past history of diabetes or myocardial infarction, use of medications), lifestyle risk factors (e.g. physical

The size of the sample required in any survey must be estimated early in the planning phase in relation to major objectives. Standard formulae are used to calculate sample size estimates, or published tables or computer programs can be consulted (21, 39, 40). The formula used will vary depending on the nature of the study hypothesis, and it is prudent for the inexpert to seek statistical advice. Calculation of sample size will require designation of parameters including an estimate of the major study endpoint(s) (e.g. difference in prevalence or mean values of a continuous variable between two subgroups), the level of statistical significance (usually 0.05) accepted as indicating a real difference, and the power or likelihood of the study demonstrating such a difference if it really does exist. With more elaborate hypotheses, and

Choice of Study Variables and Measurement Instruments

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activity, alcohol consumption, cigarette smoking, dietary fat consumption), physical measurements (e.g. height, weight, waist and hip circumferences, blood pressure), clinical examination findings (e.g. deep tendon reflexes, ophthalmoscopy), biochemical determinations (e.g. plasma glucose, serum insulin, urinary albumin concentration), and special investigations (e.g. Minnesota codings of electrocardiograms, vibration thresholds, gradings of retinal photographs) (13, 14, 20, 26 ±34, 41). Each variable selected for measurement in a survey should have a clear written operational definition which can be understood by survey staff and potentially reproduced by other investigators (42). This applies not only to physical measurements such as blood pressure and girth, but also to questionnaire variables. It is the nature of field surveys that compromises in measurement technique are often necessary, compared to what might be performed in a clinical or laboratory study, but it is equally true that particular procedures, or perhaps the whole survey, will not be worth performing if methods are suspect. Good measurements have the characteristics of repeatability and validity. Poor repeatability necessarily also implies poor validity, but a measurement may be highly repeatable, but have poor validity Ðfor instance, repeat measurements made on a glucose analyser or a weighing scale may be very similar, but if the instruments are incorrectly calibrated then they will not be valid measures. Armstrong et al (42) have described methods for assessing validity and repeatability of measurements before and during surveys. Important characteristics of a measurement in the context of a field survey are as follows: 1. Validity Ð refers to the extent to which a method measures what it is supposed to: its `truthfulness' (42). Valid measures should be both sensitive and specific (43). Particular care should be taken to check that measurement instruments adopted from elsewhere are valid. For example, established questionnaires may not be valid in different cultures, and glucose analysers may not perform well under conditions of high humidity or temperature. 2. Repeatability or reliability Ð is the level of agreement between replicate measurements (42). It is influenced by two main components:

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a. Within-subject variability: this is usually random, but may be subject to bias by factors such as exertion, cigarette smoking, stress and temperature. For example, the well-known variability in glucose tolerance test results in individuals, even when repeated within a few days, is likely to reflect both random and non-random forces (44). b. Observer or measurement variability: within single observers and=or instruments variability tends to be random, but between observers and=or instruments bias is introduced to measurements. Random measurement errors diminish true associations in epidemiological studies, but are not inherently fatal as they tend to average out. However, the systematic errors introduced by different observers and=or instruments can invalidate a study (42, 43). For example, even where standardized training of blood pressure observers and calibration of sphygmomanometers has been meticulous, it is important to ensure that subjects present to the observers randomly (21). Otherwise, if a difference between subgroups is found it will be difficult to tell if this is real or due to observer variation. 3. Robustness Ðparticularly if working in underdeveloped country settings, measurement instruments must be chosen taking into account aspects such as their portability, durability, ability to withstand electricity fluctuations and extremes of temperature and humidity, and availability of spare parts and ease of repair under field conditions. 4. Acceptability to subjectsÐbad news travels fast, and it is important not to use methods that might prejudice the cooperation of study subjects. Initially, considerable time should be spent explaining the purpose of the study and the procedures it will entail to the population, by means such as community meetings, houseto-house visits and, if subjects are literate, written explanations. Methods may need to be modified to suit local cultural factors. For instance, venesection may need to be replaced by finger pricks, or multiple samples sacrificed in favour of a single fasting or 2-hour post-load sample (20, 45). Similarly, in some cultures

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

questions regarding sensitive matters such as stillbirths and miscarriages are best avoided. In general, time-consuming and invasive procedures should be avoided in field surveys. 5. Acceptability to researchers Ð factors including cost, time to perform the measurement and ease of use under field conditions must be considered. The time needed to complete each measurement procedure, the number of procedures to be undertaken and the number of staff=measurement instrument combinations available are critical in determining the number of subjects that can be invited to a diabetes field survey in any one day (18, 20). This is especially so in surveys utilizing glucose tolerance tests in subjects who have fasted overnight, where it is advisable that all procedures, including the final 2-hour blood collection, should be completed by midday at the latest. As has been described previously, with good planning and sufficient staff it is possible to survey 120± 150 subjects per morning in a quite comprehensive diabetes and cardiovascular disease risk factor survey (18, 20, 27, 41, 46). The procedures included in such a survey are listed in Table 24.2.

Table 24.2 Sequence of procedures included in a large-scale Type 2 diabetes field survey, requiring 15±20 team members and allowing 120± 150 subjects to undertake `core' procedures Procedure

Number of staff

Core Activities 1. Registration 2. Venesection (fasting and 2-hour) 3. Glucose load 4. Height and weight 5. Waist and hip girth 6. General questionnaire 7. Activity=dietary questionnaire * 8. Blood pressure 9. Electrocardiogram * 10. Laboratory

2±3 3 1 1 1 1 1 2 2 2±4

Complications Screening * (all diabetic subjects and sample of IGT and normals: performed on day following core procedures) 1. Early morning urine Ð 2. Visual acuity & mydriasis 1 3. Foot examination=sensory testing 1 4. Eye examination=retinal photography 2 * Non-essential procedures, depending on population and resources. For further details see reference (20).

Design of Questionnaires Questionnaires should be prepared according to a set of general principles which have been well described by others (42, 47). Important aspects are summarized below: 1. Begin by listing the information required, then draft questions based on this list. 2. Questions should be easily understood, unambiguous, and non-judgemental. 3. Avoid leading questions (those that suggest an answer). 4. The questions should occur in a natural order. 5. Easy questions should come first, and more difficult and sensitive questions toward the end. 6. The relative merits of self-administered versus interviewer-administered formats must be considered. Interviewer-administered questionnaires are usually favoured in field surveys. 7. In general, a mixture of open and closed questions are used in field surveys. Closed questions are often preferable as they are more specific and easily analysed. The distinction between open and closed is in fact blurred for simple `open' questions such as, `What treatment are you currently taking regularly for diabetes?', where the interviewer records the answer directly into a coding box based on category options on the questionnaire. Where necessary, supplementary questions will be asked to guide the subject's response. Categories must be mutually exclusive (for example: yes, no, don't know; and Æ10, 11 ±20, 21± 30, etc. for cigarettes per day). 8. As far as possible questionnaires should be pre-coded, with responses written directly into boxes ready for computer entry. 9. Questionnaires should be tested for validity and repeatability and modified where necessary (42). 10. Translated questionnaires should be independently back-translated to the original language to ensure original meanings have been retained. 11. Interviewers should have clear written instructions and be trained in the use of the questionnaire.

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FIELD SURVEYS: THEORY AND PRACTICAL ASPECTS Table 24.3 Information which should be included in a manual for a field survey of Type 2 diabetes 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

The main objectives of the study. A schedule from commencement to completion. A list of personnel and their duties. Details of sample selection, and if relevant, instructions for performing the census to enumerate the sample. Clear step-by-step instructions for each procedure included in the survey (registration, venesection, glucose load, height and weight measurement, etc.). A flowchart of the sequence in which procedures should be undertaken. A pictorial representation of the preferred layout of the survey site. A chart detailing precisely how all blood, urine or other biological specimens should be processed, including information on centrifugation, number and size of aliquots, labelling, storage temperature, location of storage, and ultimate purpose and destination for each aliquot. Details of quality assurance for each procedure. A statement describing the ethical clearance for the survey. An outline of arrangements for entry and editing of data, and the likely format of main statistical analyses. A policy on preparation of publications (particularly for large studies involving multiple investigators). Copies of survey questionnaires, data record forms, invitation letters and other survey information forms.

Preparation of Survey Manual, Data Record Forms and Other Documentation

1. Study protocol=Survey manualÐthis is absolutely crucial, and should provide clear and detailed information to guide staff during preparation for the survey, throughout the field-work, during analysis and report-writing, and for the planning of longitudinal phases of the study, even if the latter might not have been part of the original design. It should be sufficiently detailed that workers not involved in the original study can understand and

reproduce the procedures. A model manual for a diabetes field survey, based on the author's experience performing surveys in several countries, has been published (20). Table 24.3 lists the type of information that should appear in a study protocol=survey manual. 2. Sample names listÐregardless of whether the study sample is derived from a pre-existing sampling frame, such as an electoral roll, or from a census performed specifically for the study (see later), a discrete listing of the group of persons to be invited to the survey must be available. This should contain columns for family name, given name, age and=or date-ofbirth, sex, address, date of invitation, survey number (a unique number allocated on arrival at the survey site, which also acts as an attendance indicator) and, depending on circumstances, additional information such as employer's name and address, and identifying numbers (e.g. for linkage purposes in a longitudinal study). The list might be arranged alphabetically by family name, or particularly in underdeveloped countries where a house-to-house census has been performed, and invitations to attend are issued in that manner, by house-hold. An example of an alphabetical listing follows.

Linkage No.

Surname

Given name

Sex

Birth date

Address

Employer details

1358 nil 612 and so on

Lawson Lazarus Lewis

Henry Paul Christina

M M F

2=6=42 21=1=58 17=4=62

77 Harper St 21 Newell St 35 Uduc Rd

Smith & Co Green's Meats Cook's Deli

A well organized survey requires the preparation in advance of not only questionnaires and data forms, but numerous other items including letters to subjects, staff instructions, equipment lists, and means for documenting the process of the survey. Particularly when working in remote locations, it is essential to ensure that sufficient forms have been printed or photocopied in advance, as facilities to generate extra copies may not be available. Important items are discussed below. Core Documents

Invite date

Survey no.

5=8 9=8 3=8

432 946 dna

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3. Survey registration book Ðas subjects are registered on arrival at the survey site they should be allocated a unique sequential number, and have their name recorded against that number, which should also be transcribed to the survey names list or census, as described above. The registration book is crucially important, as it is the `bible' for linking names to survey numbers. 4. Questionnaires=data record formsÐ in surveys where a number of procedures are undertaken sequentially, including a simple questionnaire and measurements such as anthropometry and blood pressure, it is convenient to have a single form which subjects carry with them from station to station (20). Subjects are instructed to return this form to the registration station after they have completed all procedures, where it is checked to ensure nothing has been missed. Where special procedures are undertaken (e.g. retinal photography, electrocardiograms, more elaborate questionnaires) these records are best retained at the station where they are performed, after ensuring that the subject's survey number has been transcribed, and check-boxes on the main form are ticked to indicate that the station has been visited. This allows staff to determine readily at any time which survey stations a subject has or has not visited. In some situations, such as where groups of subjects may undergo different procedures (e.g. retinal photography only for diabetic subjects, or a systematic subsample of every fifth subject for a dietary questionnaire) it may be convenient to indicate this by using differently coloured primary record forms, or by attaching a coloured sticker to the front of the forms. Survey record forms should be designed with data processing in mind Ðthis means as much of the information as possible should be precoded, preferably into boxes down the righthand side of the page to facilitate data entry to computer. As far as possible, items on survey forms should follow the sequence in which the data are collected. Particular care must be taken to ensure that the correct number of boxes are shown for measurement variables, decimal points are indicated where relevant, and staff are instructed in how to complete the forms.

Subject Information Forms A variety of forms are required for keeping subjects informed throughout the phases of a study. These help to ensure both a good response and the collection of optimal data on the subjects who attend. Wherever possible, it is a good idea to have these forms signed by respected and important figures, such as the Minister of Health, the Chief Medical Officer, and=or local civic leaders. In non-literate societies personal visits and=or community meetings will be necessary to give information. 1. Preliminary information sheet Ð this should be distributed well in advance of the survey, with the purpose of explaining the general rationale for the study, and what it will entail for the individual. At least in developing countries, it is usually best for this to be delivered by hand at the time that the house-to-house census is being performed, and accompanied by a verbal description of the survey and its purpose. 2. Invitation letter Ð this should give instructions about fasting, details of where and when to attend, an offer of transport if required, and a contact so that arrangements can be changed if the original appointment is not convenient. It is a good idea to also have a separate reinvitation letter to be used for those subjects who do not attend on their scheduled dayÐ this will be of similar format to the invitation letter. 3. Letter for employer Ðit can be an advantage for an official explanatory letter to be sent to the employers of subjects selected for the survey so as to forewarn them and to seek their cooperation in allowing their employees to take time off to attend the survey. 4. Attendance certificate Ð this is given to subjects who subsequently require proof of attendance for their employers. 5. Consent form Ð in literate societies it is usually most convenient to have subjects sign a consent form at the time they arrive at the survey Registration Station. If morbidity= mortality surveillance is contemplated for some time in the future, then it is also useful to include in the consent form the subject's permission for access to his=her medical

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records. For non-literate societies, ethics committees will generally accept that attendance at a survey is sufficient indication of consent, providing researchers undertake to give a full verbal description of the purpose and process of the survey to participants. If necessary, thumbprints can be taken. 6. Special invitations and instructions Ðdepending on the survey protocol, there may be a requirement to re-invite some survey participants on another occasion for follow-up procedures and=or substudies. For example, glucose tolerance test results may not be available until subjects have left the survey site. In this situation, newly-diagnosed diabetics could be invited back on another day for an eye examination and retinal photography. Similarly, in literate societies subjects selected to provide 24-hour, overnight, or early morning urine samples for albumin measurement should be given written (and verbal) instructions on how to collect the specimen. 7. Results notification ÐAs soon as possible after a subject's attendance they should receive a copy of their results, along with recommendations for action, including referral to medical or other practitioners where indicated. In surveys in remote locations it has been the author's habit to write results for those measures immediately available (e.g. obesity, blood pressure, glucose tolerance and retinal examination) into pre-prepared forms in the afternoon following completion of the day's work. These are then handed out by local workers. Results for other measures such as urinary albumin and serum lipids are then distributed as soon as they are available: in some situations only to those subjects requiring follow-up (subjects are told that if they hear no more it means their other results are

satisfactory). Alternatively, and particularly in developed country settings where results for routine biochemistry become available, and data are computerized, within a day or so of the survey, it is possible to print computergenerated results letters. Equipment and Supplies Documentation 1. Equipment and supplies list Ð this should be formulated well in advance (usually by many months) of the survey. Particularly when working in developing countries and remote locations, it is important to consider all contingencies, including back-ups and spare parts in case of equipment failure, and overordering of consumables to circumvent unforeseen events. Unless it is known with certainty that specific items are available in the area in which the survey is to be performed, it is prudent to take everything. This applies equally to items ranging from electric generators and tables and chairs, to elastic bands, string and pencil sharpeners! Depending on circumstances, the list might conveniently be subdivided into sections for equipment, consumables, and stationery, and should detail the exact quantity required, the dates when the item was respectively ordered, received in store, packed, and shipped, and the carton number for ease of location. An example is shown below. 2. Packing listÐwhenever a survey is to be performed at a site distant from the researcher's home base, whether this be in a different suburb or city, a rural region, or in an overseas location, a diverse range of equipment and supplies are likely to be transported. It is essential that all items contained in each

SURVEY EQUIPMENT AND SUPPLY LIST: CONSUMABLES

Survey: Shangri La 1997 Quantity Item 20  rolls 240  sachets 10  bottle 4,400 and so on

TapeÐ Micropore, 12 mm YSI buffer, 2357 15 ml=bottle Mydriacyl 1% Needle, multi, G21  1.5 (BD 7213)

Ordered

In store

Packed

Carton no.

Shipped

24=2=97 18=2=97 27=2=97 20=2=97

27=2=97 1=3=97 4=3=97 29=2=97

5=3=97 10=3=97 5=3=97 5=3=97

7 10 7 6

21=3=97 21=3=97 21=3=97 21=3=97

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carton, box or container are listed, by carton number. Cartons must be labelled clearly with this number to ensure that items can be readily located when required. No worker will enjoy having to search the contents of 25 cartons to locate an item such as a stethoscope or a pair of scissors, particularly if they are required urgently. The packing list (with a column added for monetary value) may also usually be required for insurance purposes and by shipping and=or customs agents when goods are to be freighted by land, sea or air. Survey Process and Laboratory Documentation 1. Glucose load book Ð as described previously (20), in a field survey scenario where many subjects are being given glucose loads on the same morning, it is necessary to maintain a book which lists subjects in order of the time their post-load blood tests are due, so that a staff-member can locate them and ensure that their blood samples are taken on time. This is essential, irrespective of whether times are also recorded on the subject's survey form. An example follows for a survey in which the first fasting sample on a given day was taken at 7.01 a.m.: Survey No.

Given name

Family name

247 250 249 248 251 and so on

Austin Amy Hurtle Laura Elyot

Robertson Parker Duffield Trevelyan Standish

Date: Monday 9th April

2. Laboratory results book(s)ÐThe manner in which laboratory results are recorded and stored will depend on the type of specimens collected, the measurement method and instrument used, and the location of measurement. For instance, if plasma glucose is being measured on collected blood samples in the field, or at a centralized laboratory, using a dedicated glucose analyser with manual recording of results, then they should be listed by survey number in a book dedicated to that purpose (20). This book should also record quality assurance details, including times of calibration of instruments, regular duplicate readings, regular readings for known external controls, and if applicable, information regarding daily temperature and maintenance of equipment, including changes in membranes, buffers, etc., as shown at the bottom of this page. Results can subsequently be computer entered directly from this book, or from individual survey forms if results are thus transcribed. In less sophisticated surveys using portable reflectance meters on capillary blood, glucose values could potentially be written directly into individual survey forms at the time of sampling. Nevertheless, a systematic record of details such as temperature, calibration, and changes in batch of test strips should be maintained. 2-hour due

Taken?

9.01 9.03 9.06 Ð 9.07

3 3 3 7 3

Survey Site: Port Mathurin, Rodrigues

Survey No.

Fasting

2-hour

140 141 142 143 144 and so on

5.6 (5.5) 7.1 4.8 12.5 5.9

6.2 (6.2) 13.5 5.7 Ð 9.1

Calibration

External controls

10.0, 25.1

4.8, 12.5, 28.9

Comments 2 mins late known diabetes

Temp: 27  C Notes new membrane: batch 4301 new diabetic known diabetic IGT (fasting slight haemolysis)

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Similar principles apply to any other biochemical determinations, whether performed in field or centralized laboratories. Where field researchers have contracted external laboratories to perform biochemical determinations it is important to clarify in advance details including how results will be returned to the researchers (e.g. handwritten original versus typed from handwritten versus computer printout or computer diskette from automated analyser) and the quality assurance procedures in place in the laboratory. 3. Sample and aliquot lists Ðwhether specimens are processed (e.g. centrifugation and aliquotting of blood and urine samples) at the field laboratory or at a centralized facility after transportation, a record must be maintained for each subject of which samples were received, and the number of aliquots of any specimen. In situations where the vast majority of subjects will have a particular set of specimens and=or standard number of aliquots, it is most efficient to only record variations. There will be two types of variations: those in which a `universal' sample is missing for some reason (e.g. unable to venesect, difficult venesection with volume of sample sufficient for one aliquot only); and those where particular types of specimens are collected from a subsample of subjects only (e.g. HbA1C in known diabetics only, first morning urine samples for microalbumin concentration in known and newly diagnosed diabetics, all subjects with impaired glucose tolerance, and 1 in 5 subjects with normal glucose tolerance). As an example, consider a survey where all subjects should have: (1) a fasting blood

Fasting glucose Missing

167

and so on

Fasting serum

Aliquot present 1=10 QA

Missing

10 20 30 50

12 45 134 257

2 hour glucose

Aliquots missing 1

2 7

collection comprising 2 ml fluoride=oxalate tube (plasma glucose measured on site, every tenth aliquotted for quality assurance only) and 10 ml plain tube (3 serum aliquots of 2 ml, 1.5 ml and 1.5 ml, depending on availability); and (2) a 2-hour collection comprising 2 ml fluoride oxalate tube (plasma glucose measured on site, every tenth aliquotted for quality assurance only) and 5 ml plain tube (1 serum aliquot of 2.5 ml). In addition, a systematic subsample of every fifth subject has a 5 ml sodium citrate tube taken for measurement of haemostatic factors, and all known and newly diagnosed diabetics have EDTA tubes taken for HbA1C determination. A suggested system would be as follows: (1) maintain a working laboratory notebook, in which notes on variations are recorded on a daily basis, with a separate page for each day; (2) note any missing tube types for the `universal' samples (fasting= 2 hour, fluoride=plain) and variations to the optimal number of aliquots, by survey number; (3) note all collections for the special substudy groups; (4) at the end of the survey a summary typed list of such variations for all days of the survey can be prepared, as illustrated below. 4. Other documentation Ðdepending on circumstances, a range of other information may be recorded, such as lists of subjects recalled for special studies, notes on unusual occurrences (e.g. subjects fainting during venesection, investigations of samples with duplicate survey numbers, times of electricity failures and fluctuations, etc.), and names of individuals and organizations to be acknowledged for assisting with the promotion and conduct of the survey.

Na Citrate

EDTA

Missing

Aliquot present 1=10 QA

Missing

Aliquot missing 1

Aliquot present

Present

38KD 82KD 104 168KD

10 20 30 40

38 82 104 168

7 7 7 7

5 10 15 20

38 56 82 168

3 7 7 7 7

2 hour serum

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Planning Analyses and Data Presentation Early in the planning phase of a survey it is prudent to consider the type of analyses and form of data presentation (in tables and figures) required to address the major objectives and hypotheses of the study. If the researchers do not have statistical expertise, then a statistician or epidemiologist used to dealing with biomedical and preferably epidemiological data should be consulted. Inexperienced investigators can also be guided by reviewing the nature of data presentation in published papers of related studies, although this will reveal only `the tip of the iceberg' of analysis which is really required. It is a useful exercise to draft dummy tables and figures in advance, and to consider whether the data to be collected will be suitable for providing this information. It is possible at this stage to identify problems such as variables which have been inadvertently omitted from the study design, other variables which are unnecessary, overambitious objectives, and deficiencies in sample size. Data entry programs should also be prepared in advance, and designed in relation to the questionnaires and data record forms. Data cleaning and editing procedures should be planned as part of this process. Some of the steps (such as range and logic checks and creation of new variables) can be pre-programmed into data entry programs, whether using available software (e.g. SPSS (48) or Epi Info (49)) or specially prepared programs. However, much of the data cleaning and editing will take place as a dedicated exercise following data entry, and it is wise to have a clear plan in place to facilitate this process. Funding Applications Early in the course of planning a survey a preliminary budget should be prepared, taking into account costs including staff salaries (including opportunity cost while away from normal duties), transport and accommodation, equipment and consumables, biochemistry and other laboratory investigations, and data processing and report preparation. Unless funding is already available, it will need to be sought. This will usually require the preparation of a formal grant application, depending

on the scope of the research project, to any of a multitude of potential sources, including a government medical research funding organization (e.g. the National Institutes of Health in the USA, the National Health and Medical Research Council in Australia), a university or private research trust, a diabetes-specific research fund (e.g. from a pharmaceutical company or a national diabetes association), or even the World Health Organization. Depending on the size and complexity of the application, and the requirements and reviewing process of the granting body, it can take anything from weeks to years to prepare and submit an application, have it reviewed, answer reviewers' concerns, and have a final decision on funding, with no guarantee of success. Investigators must take into account the probability and likely time-line of securing funding when formulating their research questions and study design. One way of limiting the burden is to share costs between collaborating organizations. In this way, it may be possible to proceed, particularly with small-scale studies, without the need for supplementary funding applications. It is vitally important to secure the collaboration of local health departments and other agencies which may be able to provide staff and logistic support (e.g. vehicles, office facilities). Ethics Approval Even if funding is already available, researchers have an obligation to have their research design examined and approved by a recognized independent ethics committee Ðthese are usually attached to universities and teaching hospitals. Investigators working in overseas locations should also seek approval from local authorities, whether or not formal ethics committees exist. In developing countries, responsible researchers should assist local authorities to establish ethics review committees if they do not already exist. The general principles which guide ethics in biomedical research have their foundation in the United Nations Universal Declaration of Basic Human Rights and articulated in the Declaration of Helsinki and by the Council for International Organizations of Medical Sciences (42, 50). Major issues in epidemiological research include the importance of free and informed consent, confidentiality and respect for privacy, avoidance of

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physical or psychological damage to the subject, communication of results to subjects, and the necessity that the research has practical and scientific integrity (42). ORGANISATION AND CONDUCT Logistics: Dates, Staff, Survey Sites, Equipment and Supplies 1. Dates and duration Ðthe time to be set aside for a survey will depend on several factors, including the sample size, the number of staff available, local weather patterns, and the dates of school and other holidays. Based on staffing numbers, sample size, and the time taken to complete each survey procedure per staff= subject unit, it is possible to calculate the number of subjects that can be surveyed in a day, and the number of days required overall. Where surveys are to be performed in overseas locations and=or to involve a number of collaborating investigators, it is absolutely crucial to involve all parties in the decisionmaking process. There is no glory in organizing a field survey during the cyclone season in a tropical country, during Ramadan in a Muslim country, or when necessary staff cannot be released from normal duties. The duration of the survey must be long enough for the sample size to be surveyed using available resources. It is important to factor spare days into the schedule so that time can be made up for days lost due to unforeseen circumstances, such as local funerals, transport breakdowns, and so on. A clear schedule of dates and locations must be prepared and written into the survey protocol in situations where there are multiple survey sites (20). 2. Survey staffÐit is necessary to determine that a sufficient number of appropriately qualified staff are available and `booked' early in the planning phase. For surveys performed in overseas countries or remote from the investigator's home base this usually requires a heavy reliance on staff from the local health department, university, or the likeÐmost importantly for fostering local participation in and ownership of the research, for their familiarity with customs and=or language, and for a positive

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effect on responseÐbut also for cost reasons. Equally, however, it is usually important that a nucleus of key staff from organizing institutions be in the team to undertake critical procedures and coordinate other staff. Depending on the scope of the survey, a range of nurses and other health workers, laboratory technicians, clerks, drivers, and so on, may be required. Nevertheless, many procedures (such as measuring height, weight and circumferences, interviewing, and marshalling subjects) can be undertaken by anyone with reasonable literacy and numeracy skills. It is often therefore possible, and even advantageous, to employ persons directly from the community being surveyed (such as unemployed school leavers, retired persons, civic leaders) to work as part of the survey team. Where appropriate, flights, accommodation, visas, immunizations, and so on, must be arranged well in advance for staff participating in the survey. Failure to do so may undo much organization. 3. Survey sites Ðsurvey areas should be visited well in advance, and suitable locations for conducting the survey should be identified and, if necessary, booked. Ideally, the survey site will be centrally located within the residential area of the survey sample, with road access: even if subjects walk to the survey site, some may require assistance with transport, and staff, equipment and supplies will need delivery. Where several clusters or communities are to be studied, unless the distance between them is very small, or transport is to be provided for participants, the survey team should establish a survey site in each area (12, 20, 27, 29). Facilities required will depend on the scope of the survey, but access to reliable electricity (e.g. for centrifuge, glucose analyser, ECG machine, retinal camera), running water and toilets is usually important. In areas where there is no electricity, portable generators may be required to power even sophisticated equipment, or battery-operated instruments can be used. For large surveys, school buildings (during holidays) are ideal, as procedures can be located in sequence in different classrooms, allowing orderly subject flow. However, community and church halls, health centres, private houses, open verandahs and even outdoor areas can be used, depending on

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Figure 24.1 Example layout for a field survey of Type 2 diabetes and cardiovascular disease risk factors, as performed in large community halls in Singapore Source: Singapore: Ministry of Health, 1992 (51). Reproduced by permission

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circumstances. It may be necessary to provide temporary screens for privacy for procedures such as venesection, girth measurements and electrocardiograms. A quiet area should also be identified for blood pressure measurement, away from noisy waiting areas. Figure 24.1 shows an example of survey site lay-out for a typical diabetes prevalence study. Figures 24.2 ±24.5 demonstrate scenes from diabetes field surveys performed in developing countries. 4. Equipment and supplies Ð the principles involved in the choice of measurement instruments and the importance of maintaining accurate documentation of equipment and supplies have been discussed earlier in this chapter. Of no less importance is the practical process of defining exactly what is required to undertake the survey, assuming the critical measurement instruments have already been defined. This entails detailed consideration of

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each procedure to be undertaken, and meticulous recording of all items necessary to carry out that procedure. Pilot testing will help to identify missing items. For example, for the Registration Station the list may entail: a descriptive sign; a large table with 4 chairs (sufficient for 2 staff and 2 subjects at a time); pencils, erasers, and pencil sharpeners; paperweights; sufficient survey forms, employer certificates, consent forms, reminder forms, etc. for all subjects; coloured stickers to identify subjects selected for substudies; the census list; the registration book, rubber bands for separating each day's survey forms; and a wastepaper bin. Each time a survey site is established, the checklist of items required for each procedure should be used to ensure readiness. As for measurement instruments, cost, robustness, and acceptability to subjects and staff must be considered when choosing

Figure 24.2 Subjects sitting awaiting blood pressure measurement during a survey performed at a school building in the Indian Ocean island of Rodrigues

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Figure 24.3 Venesection station established in a community hall during a field survey in the Indian Ocean island of Mauritius. At a busy time of the morning, four staff are collecting blood samples

supplies. For example, the relative merits of needles and syringes versus vacuum tubes with multi-sample needles must be weighed up for venesection Ðcertainly for busy surveys a reliable brand of the latter are favoured, even if they may be more expensive. Similarly, the choice between glass and plastic disposable pipettes will likely be decided in favour of the latter when a field laboratory is to be established in a remote area involving transport over rough terrain, and higher quality serum storage tubes will be selected where samples are to be stored for long periods at very low temperatures. When performing surveys away from home territory, unless it is known with certainty that specific items are available at the destination, it is wise to err on the side of caution, and take absolutely everything required. Moreover, sufficient spare parts and extra supplies should be packed to cover misadventure.

Pre-testing, Pilot-testing and Staff Training Pre-testing is performed during the early stages of planning when measurement instruments (e.g. sphygmomanometers, height and weight scales, questionnaires, glucose analysers) are being selected and survey procedure is being defined. Instruments will be assessed for their validity, repeatability, robustness and acceptability to subjects and staff, as discussed earlier. Where there are clear options different types of instruments should be compared, and=or assessed in relation to a `gold-standard'. Staff must be trained assiduously in the standardized methods of the task(s) which they will undertake in the survey, following the instructions detailed in the survey manual (18, 20, 21, 42). No procedure should be exempted from strict standardization and training, including venesection, measurement of height and weight and blood pressure, and laboratory processing of samples. Variation

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Figure 24.4 Glucose load station in a vacant hospital ward used as a survey site in the Pacific Island nation of Western Samoa. Glucose monohydrate solution prepared in bulk is dispensed into cups for subjects

within and between observers should be assessed, and staff who perform unsatisfactorily in training should be replaced (42). Measurements which are subject to a large amount of observer variation, such as blood pressure, require relatively more effort to achieve standardization (21). Methods for training blood pressure observers have been devised which use standard audiotapes and=or videotapes, and double-headed stethoscopes. Of course, variation due to differences between observers can be limited by having as few observers as possible performing each measurement or procedure. Where there must be more than one observer, it is important to ensure that different groups of subjects (e.g. defined by village, gender, ethnicity or age-group, etc.) do not present preferentially to one observer or another. If this is the case, even with proper training and standardization, if a difference is subsequently found between the groups in the measured parameter it may be difficult to exclude systematic measurement bias between observers as being responsible.

Staff training should also include an understanding of the overall goals and structure of the survey, the importance of maintaining subject confidentiality, and an emphasis on the benefits of being professional, but courteous to participating subjects. Once staff have been trained, equipment has been selected, and all is in readiness, it is important to have a formal pilot-test of the survey procedure. This should not be undertaken on subjects eligible for the real survey. The pilot test is a dress rehearsal for the actual survey, and is a final opportunity to refine techniques, to identify and correct bottlenecks in the flow of subjects through the procedures, and to reassess practical matters such as space requirements, placement of chairs and tables, and adequacy of directional signs. Census of Survey Population Unless a pre-existing sampling frame is available, such as a current electoral roll, it is usually

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Figure 24.5 Glucose measurement at a temporary laboratory established at a school building during a field survey

necessary to perform a census prior to the conduct of a survey. In whole community or cluster surveys this necessitates systematically visiting houses one by one, and recording details for all individuals who are eligible for the survey (20). This may require several visits. In rural and remote areas, and even in urban areas of developing countries, it may be necessary to allocate numbers to dwellings and draw schematic maps which show houses in relation to each other and landmarks such as roads, churches, school-buildings, trees and rivers. This information is then used during the survey for issuing invitations and giving results, and is invaluable if follow-up studies are to be performed. These methods have been applied often in surveys of Type 2 diabetes in South Pacific island nations (5, 26, 27, 29). The household census also becomes the sampling frame should there be any additional random or systematic sampling stages, and defines the denominator for assessment of response and representativeness of the eventual survey population. The preparation and use of the nominal roll

for participation in the survey, based on the original household survey, has been described earlier. Promotion and Maximizing Response The value of a survey's findings depends to a large extent on achieving a high response rate, such that significant bias in results will not occur even if the non-responder group are not representative (21, 42). Response rates of 80± 100% have commonly been achieved in diabetes field surveys in rural areas of developing countries, but in general response tends to be lower in more urbanized subgroups and developed populations where the pace of life is faster and opportunity for distraction is greater (5, 10 ± 12, 26, 27, 37, 38). To maximize response it is important to plan and implement a promotional campaign aimed at explaining to the study population the overall goals of the survey, what it will entail for them, and what benefits it might bring to the community

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and to themselves. Prominent members of the community such as politicians, health officials, and religious and civic leaders should be approached for support. It is also important to involve the local medical profession in the study, as otherwise resentment at perceived `invasion of their patch' may adversely affect response. A local `motivator' should be appointed for each survey area, preferably somebody who knows the community well and who has wide respect. This person may conduct the initial census, and then continue in the role of raising awareness and distributing invitations, reminders, results letters, and so on, throughout the period of the study. Where feasible, all available avenues of the media (variably including garamut drum, newspapers, newsletters, radio and television) should be used, including free media releases and interviews, and paid advertisements. Posters and banners can be placed in prominent locations. Eligible households and=or individuals should receive a clear and honest explanation of the nature of the survey well in advance, both written and verbally. This should provide details such as the time required, the procedures to be undertaken and any risks involved, and special requirements such as the need for fasting and abstention from smoking. Positive aspects should also be highlighted, including: the availability of free medical investigations; the promise that results will be returned to subjects promptly, and that treatment or referral will be arranged wherever necessary; the availability of transport to and from the survey site if required; and the issuing of medical certificates for employers. The information should then be reinforced a few days prior to the actual day of the individual's scheduled attendance, and perhaps again on the preceding evening. In general, it is best not to offer financial or other rewards for attendance. However, in a survey in which subjects have fasted and probably given up 2 or more hours of their time, it is quite acceptable to offer them a light meal or refreshment once they have completed all their procedures. It is important, however, that this be kept away from the general survey area and carefully `policed', lest subjects eat or drink something prior to completion of their glucose tolerance test. It is reasonable to give non-responders one personal reminder while the survey team remains in the area. Additional reminders are unlikely to be

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very fruitful. If response is found to be poor at the beginning of a survey, discussions should be held promptly with local team members, community leaders and non-responders to try to determine whether any particular fears, problems (e.g. lack of transport or babysitting,) or miscommunication might be responsible that can be corrected. At conclusion of the survey known demographic characteristics of responders and non-responders (as determined in the household census) should be compared in order to detect any obvious response bias (e.g. in relation to gender, age, ethnicity or occupation). Moreover, all or at least a subsample of non-responders should be contacted and asked: (1) to identify their reason for non-attendance (e.g. dead, too ill, unable to take time off work, not interested); and (2) whether they have a history of any of the endpoints of major interest within the survey (e.g. history of smoking, diabetes, hypertension, or myocardial infarction). Such information can then be compared with that of the responding sample, and judgement made as to whether any differences found might be sufficient to significantly bias the survey results (21, 42). Supervision and Quality Control The survey team should have a clearly designated coordinator, who must be intimately aware of the survey protocol and the requirements for each procedure. Ideally, the coordinator should have a roving commission to answer questions from staff and participants, to observe the performance of staff and the flow of subjects at each measuring station, to correct problems as they are identified, and to offer encouragement to staff. It is best that the coordinator is not allocated to one of the measurement procedures, although they should be trained, certified and able to fill in at any position in case of emergency (e.g. allocated staff taken sick). Survey forms and other records, including registration and glucose load books and laboratory documentation, should be examined regularly Ð certainly at the end of each day of the survey Ð to ensure completeness, to perform any secondary coding required, and to identify any problems which can be corrected. This includes difficulties with legibility, accidental errors and omissions, and systematic errors. Where more than one

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observer performs a particular measurement procedure, they should each be allocated a unique observer number which is recorded on the survey form adjacent to the measurement; in this way problems can be traced back to the observer and appropriate action, including retraining and restandardization, undertaken. If data are being computer entered while the survey is in progress, more formal observer checks can also be performed. For example, terminal digit preference can be assessed, and assuming random allocation of subjects, systematic differences between observers and drift over time can be imputed if there are significant differences in mean values and distributions of measured variables, such as blood pressure. Reliability of data can also be assessed by repeating at least some measurements on a random sample of subjects. This includes the use of duplicate split samples, unidentifiable to the laboratory, for biochemical measurements. Armstrong et al. (42) have outlined the importance of ongoing quality control at all stages of survey preparation and conduct, including data processing. It is advisable for team meetings to be held at intervals during a survey, when matters of concern and possible solutions can be discussed. Positive reinforcement and encouragement should be offered freely to staff, particularly in long and arduous surveys. Moreover, days off and social occasions should be scheduled at regular intervals to help foster team spirit and enthusiasm. In field surveys of glucose tolerance where work usually begins early in the morning, many members of the survey team may in fact finish work around midday, allowing time for preparations to be made for the following day, and for recreation (18, 20).

given. The latter are often highly regarded in developing countries. Particularly in developing countries and remote locations it is often good public relations, and even practical in terms of transportation costs, to donate leftover consumables (e.g. venesection and laboratory supplies) to local health-care facilities. This may also be extended to equipment items such as sphygmomanometers. Even if detailed analysis of the survey data may not be available for some months, it is essential to provide preliminary feedback of overall results to local authorities (e.g. medical practitioners, town= village officials, health department officers, politicians) and to the survey population. Depending on circumstances, the latter may involve a newsletter delivered to all subjects, local meetings, or use of the mass media. It is also important that a clear timetable for provision of a final report to local authorities and any other interested parties is defined before the departure of the survey team. Meeting such deadlines may involve the cooperation of a number of collaborating organizations which have responsibility for providing different aspects of the data (e.g. specific biochemical analyses). However, collation and final data analysis should be the task of the coordinating institution. When the final report is available, the process of consultation with relevant local authorities, and feedback of the general findings to the survey and general population should be repeated. This is especially important where the survey findings are to be used as the basis for development and implementation of specific interventions.

AFTER THE SURVEY

Equipment and supplies to be returned to source locations should be packed and labelled carefully, as described earlier. If air transport is required this is crucial to satisfy airline safety standards and customs obligations. Where specimens are to be transported, painstaking planning is necessary to ensure that they arrive at their destination in good condition. If transport is to take any more than a few hours then ordinary ice bricks will be inadequate to ensure specimens remain at 20  C. For longer

Public Relations and Follow-up The conclusion of a survey, particularly a long one which involves personnel from a number of institutions, can be a sad occasion, and it is appropriate to hold a party as a means of allowing farewells and acknowledging survey staff and other contributors. Depending on circumstances, gifts and=or certificates of participation should be

Packing, Transportation and Storage of Equipment and Specimens

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trips, dry ice will be required, or for samples that need to be kept very cold, liquid nitrogen. If using dry ice, whether as bricks or pellets, it is important to ensure that enough is packed in each cool box to last the expected duration of the trip, plus a margin for misadventure: the amount required should be calculated with the assistance of experienced laboratory managers. For example, a missed flight connection may mean that samples take an additional 24 hours or more to be delivered. If the itinerary is of long duration, it may be necessary to arrange in advance for replenishment of the dry ice supply en route. Certainly, wherever there is a change of flight, it is prudent to make personal contact with an individual in the local office of the cargo agency and=or airline company involved to seek their assistance in ensuring the successful transfer of the precious cargo on to the new craft. If dry ice is to be used, then the precise amount required and time of pick-up should be booked well in advance. In locations where dry ice is not manufactured, it may be necessary to arrange for it to be freighted in from elsewhere; in this case a larger amount than required should be ordered to account for wastage on the in-coming sector. If specimens are to be air-transported then the timing of collecting dry ice and packing specimens will be designed around the departure time of the flight. It is also absolutely essential that arrangements with the airline, cargo agent, and customs officials have been resolved well in advance. There are special requirements in terms of packaging, labelling and documentation that must be fulfilled when transporting biological material, and when using dry ice. If it is possible, duplicate aliquots of crucial specimens should not be transported at the same time: an aliquot should remain in storage as a back-up in case of problems. Clear instructions should be given in advance to individuals at the home base who are to receive the specimens and=or equipment. This means informing them of the itinerary of the goods, the time of arrival, air waybill numbers, where relevant, and persons to contact in the freight agency and=or airline. Moreover, the freezers in which specimens are to be stored should be identified, checked and prepared. Once the specimens and equipment have been received, their condition should be noted as they are unpacked and stored, and an acknowledgement should be sent forthwith to the despatchers.

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Specimens will usually be aliquots of serum, plasma, buffy coats or urine in small plastic storage containers. Each tube should be labelled in such a way as to identify the survey from which it derives, the type of specimen (e.g. plasma, serum, urine), the subject's unique identifier, the time of collection if multiple samples were taken during a glucose tolerance test, and the number of the aliquot where more than one are taken from a particular sample. In this way, an errant tube can be immediately identified. For example, the code K637C might identify a tube as coming from the 1991 Papua New Guinea survey (K), from subject number 637, and the specimen as the second aliquot of fasting serum (C). Care must be also be taken to ensure that labels on tubes are durable and will not fade or wash off. Specimen tubes are best packed at the time of processing during the survey, transported and stored, in numerical sequence, in clearly labelled (survey descriptor, type of sample, aliquot number, numerical range), multi-compartment (e.g. 100 spaces) cardboard boxes. The latter are available from laboratory and refrigerator suppliers, or can be purpose-made by packaging firms, often for a cheaper price. Specimens stored systematically in this manner are much more amenable to good housekeeping than those stored randomly in plastic bags, or in a motley and bulky array of foam, metal and plastic laboratory trays. Safe-keeping of Survey Forms and Other Documentation As for specimens, it is vitally important to ensure that all data and important documentation are safe during transport. If possible, questionnaire and other data should be computer entered at, or close to, the survey location. Thus, back-up copies of the electronic data record can be saved and retained in different locations. Nevertheless, hard copies of the original questionnaires, laboratory results, and documentation such as census lists, registration books, glucose load books, and so on, must also be kept for reference. If these need to be transported to the home base of investigators, then photocopies of original information should be made and retained by relevant local authorities. Not only does this provide a back-up, but it also ensures that local

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investigators have access to the information in the future should they require it Ð an important consideration when surveys are coordinated by researchers from overseas or distant locations, who despite their best intentions may subsequently lose contact with collaborators at the point of the survey. This is particularly relevant for longitudinal investigations where personnel may move to other jobs. Vital records are best carried as hand luggage: baggage does get lost and misplaced, whether transport is by private car, train or aircraft. If there are several investigators, the weight can be shared. If it is unavoidable that questionnaires or other information must be transported as baggage or freight, then the importance of ensuring photocopies and=or electronic copies are made prior to departure cannot be overstated. On return to base, all hard copies of data and other survey documents should be well labelled, and stored safely and securely. Confidentiality of records must also be maintained. Only key investigators should be able to link the identity of survey participants to their data records. As a minimum, name and address data should be stored separately, preferably in a locked cupboard or safe. Data Processing and Reporting Data entry, whether performed at the time of the survey, or following it, must be subject to strict quality control, just as for other aspects of the survey (42). Data should be entered twice, independently in time and preferably by different persons. This allows computer programs to identify discordant entries, so that the correct values can be verified from the original data record. In addition, range and logic checks can be pre-programmed into the data entry program to identify improper responses. If data entry clerks cannot resolve these at the time of the data entry, then they should proceed entering data, after noting irregularities on a `query sheet'. Problems can subsequently be investigated and resolved by the supervisor. Where clearly erroneous values on questionnaires or other records are altered during the checking process, this fact should be recorded on the original record. Additionally, `manual' changes may need to be made to some variables

for individual subjects (e.g. classification of glucose tolerance made missing for individuals recorded in the glucose load book as having vomited their drink, or having been late for the 2 hour collection). Programming code used to create new variables (e.g. categorical diabetes or hypertension status variables derived from glucose and blood pressure measurements and clinical history) should be carefully checked to ensure correctness. This includes review by other workers, and listing of a sample of subject records so that manual classification can be compared to the computed variable. A `coding book', or at least a labelled file of computer printout, should be maintained which clearly describes all variables and value labels, including the programming required to create new variables. Up-dates to the computer file should also be documented here, along with the date of any modifications. Further cleaning and editing of the dataset involves examining descriptive data such as frequency tables and simple cross-tabulations for categorical variables, and distributions and scatterplots of continuous variables (39). This is an important stage in identifying remaining errors and inconsistencies in the data, prior to creating the working dataset for definitive analysis. As discussed earlier, the sort of analyses to be performed for the purposes of initial reporting should have been considered during the planning phase of the survey, and hence once the dataset is cleaned, edited and ready, analysis should proceed promptly. The first priority is often to draft a report for use by health departments and other agencies which documents the magnitude of the problem and identifies areas of need (e.g. large pool of undiagnosed cases suggesting need for raising public awareness of risk factors and symptoms, and for improving medical casefinding; poor management of blood lipids in known diabetics, suggesting need for improved medical care and better dietary education of cases; etc.). The type of analysis required in this context is generally descriptive, and as such reports are meant for politicians and busy health officials they should be kept relatively simple, with clear messages, including the appropriate use of tabular and graphical information. Once such formal reporting obligations have been met, attention can be turned to more complex

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and focused analytic approaches and preparation of scientific articles. The best advice to inexperienced investigators preparing to write up their survey findings for medical journals is to carefully examine journal requirements and previously published articles of similar work. Of course, the major research questions to be addressed should have been defined early in the planning phase of the study, and hence will follow a largely preordained course. However, flexibility must be maintained to allow the pursuit of unexpected findings thrown up by the data, and so that new hypotheses suggested by the scientific literature can be examined.

ACKNOWLEDGEMENTS The methods and examples described are in large part derived from those used in surveys coordinated by the International Diabetes Institute, Melbourne, Australia, in several countries of the South Pacific and Indian Ocean regions. The author extends thanks to his former colleagues at the Institute, particularly Paul Zimmet, Ray Spark, Veronica Collins, and Allison Hodge, and to the many individuals from a range of countries and organizations who collaborated in performing these surveys.

REFERENCES 1. West KM. Epidemiology of Diabetes and its Vascular Lesions. New York; Elsevier, 1978. 2. Ekoe J-M. Diabetes Mellitus. Aspects of the Worldwide Epidemiology of Diabetes Mellitus and its Longterm Complications. Amsterdam; Elsevier, 1988. 3. WHO DIAMOND Project Group. The WHO multinational project for childhood diabetesÐ DIAMOND: Diabetes Mondiale. Diabetes Care (1990); 13: 1062 ±1068. 4. Karvonen M, Tuomilehto J, Libman I, La Porte R. A review of the recent epidemiological data on the worldwide incidence of Type 1 (insulin-dependent) diabetes mellitus. Diabetologia (1993); 36: 883± 892. 5. Zimmet P, Dowse G, Finch C, Serjeantson S, King H. The epidemiology and natural history of NIDDM Ðlessons from the South Pacific. Diabetes= Metabol Reviews (1990); 6: 91±124. 6. King H, Rewers M, WHO Ad Hoc Diabetes Reporting Group. Global estimates for prevalence of diabetes mellitus and impaired glucose tolerance in adults. Diabetes Care (1993); 16: 157± 177.

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7. Verge CF, Howard NJ, Irwig L, Simpson, JM, Mackerras D, Silink M. Environmental factors in childhood IDDM. A population-based, case-control study. Diabetes Care (1994); 17: 1381 ±1389. 8. Gerstein HC. Cow's milk exposure and type 1 diabetes mellitus. A critical overview of the clinical literature. Diabetes Care (1994); 17: 13 ± 19. 9. Harris MI, Klein R, Welborn TA, Knuiman MW. Onset of NIDDM occurs at least 4 ± 7 yr before clinical diagnosis. Diabetes Care (1992); 7: 815± 819. 10. Glatthaar C, Welborn TA, Stenhouse NS, GarciaWebb P. Diabetes and impaired glucose tolerance: a prevalence estimate based on the Busselton 1981 survey. Med J Aust (1985); 143: 436± 440. 11. Harris MI, Hadden WC, Knowler WC, Bennett PH. Prevalence of diabetes and impaired glucose tolerance and plasma glucose levels in US population aged 20 ± 74 yr. Diabetes (1987); 36: 523±534. 12. Dowse GK, Gareeboo H, Zimmet PZ, Alberti KGMM, Tuomilehto J, Fareed D et al. High prevalence of NIDDM and impaired glucose tolerance in Indian, Creole and Chinese Mauritians. Diabetes (1990); 39: 390± 396. 13. Collins VR, Dowse GK, Finch CF, Zimmet PZ, Linnane AW, Thoma K. Prevalence and risk factors for micro- and macro-albuminuria in diabetic subjects and entire population of Nauru. Diabetes (1989); 38: 1602 ±1610. 14. Tuomilehto J, Li N, Dowse G, Gareeboo H, Chitson P, Fareed D et al. The prevalence of coronary heart disease in the multi-ethnic and high diabetes prevalence population of Mauritius. J Intern Med (1993); 233: 187±194. 15. World Health Organization Study Group. Diabetes Mellitus. Technical Report Series 727. Geneva; WHO, 1985. 16. Al Sayegh H, Jarrett RJ. Oral glucose tolerance tests and the diagnosis of diabetes: results of a prospective study based on the Whitehall survey. Lancet (1979); ii: 431± 433. 17. Pettitt DJ, Knowler WC, Lisse JR, Bennett PH. Development of retinopathy and proteinuria in relation to plasma glucose concentration in Pima Indians. Lancet (1980); ii: 1050± 1052. 18. Dowse G, Zimmet PZ. Epidemiological research methodologies for NIDDM. In: CE Mogensen, E Standl (eds), Research Methodologies in Human Diabetes; Diabetes Forum Series, vol V. Berlin; de Gruyter, 1994: pp. 37 ± 55. 19. McCarty D, Zimmet P. Diabetes 1994 to 2010: Global Estimates and Projections. Leverkusen; Bayer AG, 1994. 20. Dowse GK, Zimmet PZ. A model protocol for a diabetes and other noncommunicable disease field survey. World Health Statist Quart (1992); 45: 360±372. 21. Rose GA, Blackburn H, Gillum RF, Prineas RJ. Cardiovascular Survey Methods, 2nd edn. WHO Monograph Series No. 56. Geneva; WHO, 1982.

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22. Bennett PH. Recommendations on the standardization of methods and reporting of tests for diabetes and its microvascular complications in epidemiologic studies. Diabetes Care (1979); 2: 98 ± 104. 23. Rose G, Keen H, Jarrett J. Epidemiologic methods in diabetic macrovascular disease. Diabetes Care (1979); 2: 91 ± 97. 24. Alderson M. An Introduction to Epidemiology, 2nd edn. London; Macmillan, 1983. 25. Abramson JH. Survey Methods in Community Medicine, 4th edn. London; Churchill Livingstone, 1990. 26. King H, Taylor R, Zimmet P, Pargeter K, Raper LR, Beriki T et al. Non-insulin-dependent diabetes (NIDDM) in a newly independent Pacific nation: the Republic of Kiribati. Diabetes Care (1984); 7: 409±415. 27. Collins VR, Dowse GK, Toelupe P, Imo T, Aloaina F, Spark RA, Zimmet PZ. Increasing prevalence of NIDDM in the Pacific island population of Western Samoa over a 13-year period. Diabetes Care (1994); 17: 288± 296. 28. Ostbye T, Welby TJ, Prior IAM, Salmond CE, Stokes YM. Type 2 (non-insulin-dependent) diabetes mellitus, migration and westernization. The Tokelau Island migrant study. Diabetologia (1989); 32: 585± 590. 29. Dowse GK, Spark RA, Mavo B, Hodge AM, Erasmus RT, Gwalimu M et al. Extraordinary prevalence of non-insulin-dependent diabetes mellitus and bimodal plasma glucose distribution in the Wanigela people of Papua New Guinea. Med J Aust (1994); 160: 767± 774. 30. Jarrett RJ, Keen H, Fuller JH, McCartney M. Worsening to diabetes in men with impaired glucose tolerance (`borderline diabetes'). Diabetologia (1979); 16: 25 ± 30. 31. Saad MF, Knowler WC, Pettitt DJ, Nelson RG, Mott DM, Bennett PH. The natural history of impaired glucose tolerance in the Pima Indians. N Engl J Med (1988); 319: 1500± 1506. 32. Dowse GK, Zimmet PZ, Collins VR. Insulin levels and the natural history of glucose intolerance in Nauruans. Diabetes (1996); 45: 1367± 1372. 33. Ohlson L-O, Larsson B, Bjorntorp P, Eriksson H, Svardsudd K, Welin L et al. Risk factors for Type 2 (non-insulin-dependent) diabetes mellitus. Thirteen and one-half years of follow-up of the participants in a study of Swedish men born in 1913. Diabetologia (1988); 31: 798± 805. 34. Hamman RF, Marshall JA, Baxter J, Kahn LB, Mayer EJ, Orleans M et al. Methods and prevalence of non-insulin-dependent diabetes mellitus in a biethnic Colorado population. The San Luis Valley diabetes study. Am J Epidemiol (1989); 129: 295±311.

35. Eriksson K-F, Lindgarde F. Prevention of Type 2 (non-insulin-dependent) diabetes mellitus by diet and physical exercise. The 6-year Malmo feasibility study. Diabetologia (1991); 34: 891± 898. 36. Welborn TA, Glatthaar C, Whittall D, Bennett S. An estimate of diabetes prevalence from a national population sample: a male excess. Med J Aust (1989); 150: 78 ± 81. 37. Zimmet P, Taylor R, Ram P, King H, Sloman G, Raper LR et al. Prevalence of diabetes and impaired glucose tolerance in the biracial (Melanesian and Indian) population of Fiji: a rural-urban comparison. Am J Epidemiol (1983); 118: 673± 688. 38. Zimmet P, King H, Taylor R, Raper LR, Balkau B, Borger J et al. The high prevalence of diabetes mellitus, impaired glucose tolerance, and diabetic retinopathy in Nauru Ðthe 1982 survey. Diabetes Res (1984); 1: 13 ± 18. 39. Armitage P, Berry G. Statistical Methods in Medical Research, 2nd edn. Oxford; Blackwell Scientific Publications, 1987. 40. Lwanga SK, Lemeshow S. Sample Size Determination in Health Studies. Geneva; WHO, 1991. 41. Collins VR, Dowse GK, Plehwe WE, Imo TT, Toelupe PM, Taylor H et al. High prevalence of diabetic retinopathy and nephropathy in Polynesians of Western Samoa. Diabetes Care (1995); 18: 1140± 1149. 42. Armstrong BK, White E, Saracci R. Principles of Exposure Measurement in Epidemiology. Oxford; Oxford University Press, 1994. 43. Barker DJP, Rose G. Epidemiology in Medical Practice, 3rd edn. Edinburgh; Churchill Livingstone, 1984. 44. Stolk RP, Orchard TJ, Grobbee DE. Why use the oral glucose tolerance test? Diabetes Care (1995); 18: 1045±1049. 45. King H, Finch C, Collins A, Koki G, King LF, Heywood P et al. Glucose tolerance in Papua New Guinea: ethnic differences associated with environmental and behavioural factors and the possible emergence of glucose intolerance in a highland community. Med J Aust (1989); 151: 204±210. 46. Dowse GK, Gareeboo H, Alberti KGMM, Zimmet PZ, Tuomilehto J, Purran A et al. Changes in population cholesterol concentrations and other cardiovascular risk factor levels after five years of the non-communicable disease intervention programme in Mauritius. Br Med J (1995); 311: 1255±1259. 47. Bennett AE, Ritchie K. Questionnaires in Medicine. London; Oxford University Press, 1975. 48. SPSS Inc. SPSS Data Entry II for the IBM PC=XT=AT and PS=2. Chicago; SPSS Inc, 1987.

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49. Dean AG, Dean JA, Coulombier D, Brendel KA, Smith DC, Burton AH, et al. Epi Info, Version 6: a word processing, database, and statistics program for epidemiology on microcomputers. Atlanta; Centers for Disease Control and Prevention, 1994. 50. CIOMS. Proposed International Guidelines for Biomedical Research Involving Human Subjects. Geneva; Council for International Organizations of Medical Sciences, 1982. 51. Research and Evaluation Department. Survey protocol and manual. National health survey, 1992. Singapore; Ministry of Health, 1992.

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52. Expert Committee on the Diagnosis and Classification of Diabetes Mellitus: Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes (1997); 20: 1183 ± 1197. 53. Report of a WHO Consultation. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and classification of diabetes mellitus. Geneva; World Health Organization, 1999.

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25

The United Kingdom Prospective Diabetes Study: An Epidemiological Perspective 1

Paul Zimmet,1 M. Cohen1 and Jean-Marie EkoeÂ2

International Diabetes Institute, Melbourne, Australia 2 Centre de Recherche-CHUM, Montreal, Canada

INTRODUCTION For several years now, there has been an air of expectancy as the international diabetes community awaited the results of the United Kingdom Prospective Diabetes Study (UKPDS) (1). The results are now in (2 ± 6) and the question is whether the results will change the way that we approach the management of Type 2 diabetes. The UKPDS study also provided, for the first time, information on the long-term natural history of Type 2 diabetes in a European population. The editors felt that a book on the epidemiology of diabetes, indeed any book dealing with diabetes, would be incomplete without reference to this monumental study, the largest and longest yet in diabetes history. Was the wait worthwhile and will the results influence therapy of Type 2 diabetes? THE UNITED KINGDOM PROSPECTIVE DIABETES STUDY Ð WHY WAS IT SO IMPORTANT? Epidemiological studies have shown an increased risk of cardiovascular disease (CVD) with concentrations of fasting glucose or HbA1c just above the normal range (7 ±9). A previous large-scale randomized trial in Type 2 diabetes, the University Group Diabetes Program (UGDP) (10), had found no evidence that improved glucose control, by any therapy, reduced the risk of cardiovascular endpoints. In fact, the UGDP reported an increased risk of CVD mortality in subjects allocated to tolbutamide and to phenformin (10). This led to an ongoing debate about the safety of sulphonylurea

therapy, and to the withdrawal of phenformin, which had also resulted in a case of fatal lactic acidosis. Another ongoing controversy has been the suggestion that there may be an increased risk of atherosclerosis from insulin therapy, or indeed any therapy that increases hyperinsulinaemia and weight gain (11). It was against this background that, commencing in 1977, the UKPDS was designed to establish whether, in people who presented clinically as having Type 2 diabetes, improving blood-glucose control reduced the risk of macrovascular or microvascular complications, and whether any particular therapy was advantageous. Earlier intervention studies have been of lesser duration and assessed mainly microvascular disease. For example, the Diabetes Control and Complication Trial (DCCT) (12), which lasted 9 years, showed that improved glucose control delays the development and progression of retinopathy, nephropathy, and neuropathy in people with Type 1 diabetes. In the UK, 9% of people with Type 2 diabetes develop micro-vascular disease within 9 years of diagnosis, but 20% had a macrovascular complication, and macrovascular disease accounts for 59% of deaths in these people (2). DOES METABOLIC CONTROL MATTER? A number of epidemiological studies have shown the importance of indices of glycaemic control and duration of diabetes in determining the prevalence and incidence of diabetic microvascular complications (13 ± 15), but this required testing in largescale clinical trials. The Diabetes Control and

The Epidemiology of Diabetes Mellitus. An International Perspective. Edited by Jean-Marie EkoeÂ, Paul Zimmet and Rhys Williams. # 2001 John Wiley & Sons Ltd.

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THE EPIDEMIOLOGY OF DIABETES MELLITUS

Complications Trial (DCCT) (12) and the Stockholm Study (16) first provided this evidence. The landmark DCCT, a randomized clinical trial, demonstrated that good glycaemic control reduces the risk of microvascular complications in Type 1 diabetes (12). A reduction of approx. 2% in HbA1c level (from a mean of 9± 7.2%) resulted in a 50± 75% reduction in the risk of microvascular complications developing, or progressing if already present. Because of to the small numbers of macrovascular events in young people with Type 1 diabetes after only 9 years, the results were inconclusive. Direct evidence that improving glucose control reduces the incidence of microvascular complications is now available and accepted from this pivotal study, along with the Stockholm study (16). A major question remained. Do these results apply to people with Type 2 diabetes? A smaller study, from Kumamoto in Japan on intensive therapy for Type 2 diabetes (17), produced similar results to the DCCT and suggested that this would be the case. The Japanese study utilized a protocol almost identical to that of the DCCT and similar benefits to the DCCT were noted with a 50± 75% reduction in microvascular complications (17). THE UKPDS FINDINGS The UKPDS study was part of a long-term diabetes research project in which 3867 newly diagnosed people with Type 2 diabetes were randomly assigned to two treatment groups. The first (conventional) group's initial treatment was dietary restriction. The second (intensive treatment) took one of three oral sulphonylurea agents (chlorpropamide, glibenclamide, or glipizide) or insulin. The intensive therapy group had better bloodglucose control, with a mean HbA1c level of 7.0% compared to 7.9% in the conventional group (2). The outcomes were `hard' clinical endpoints including severe cardiovascular events, vision loss, and renal failure (2). Even though there are differences in pathogenesis, these outcomes were grouped together as all diabetes-related events, diabetes-related mortality, and all-cause mortality, respectively. Interpretation of the UKPDS results needs to be seen against the background of several changes to the study design over the years due to a number of substudies, e.g. that for hypertension

and acarbose. Intensive-therapy subjects were 12% less likely to develop a diabetes-related complication, e.g. myocardial infarction, stroke, and microvascular complications such as retinopathy (2). There was a 25% reduction in risk of microvascular disease (mainly retinopathy.) This reduction was very similar to that seen in the DCCT (12) when adjusted for the difference in HbA1c reduction. Nevertheless, intensive therapy did not significantly reduce the risk of death from diabetesrelated causes, or overall risk of death. There was also a significant reduction in need for cataract extraction in the intensive group. All intensive treatments produced similar results, and all resulted in weight gain and increased risk of hypoglycaemia. Of practical interest was the finding that the hyperinsulinaemia and weight gain associated with intensive therapy did not translate into an increased risk of macrovascular disease (2). Indeed the trend appeared to be the reverse, with a 16% reduction in risk of macrovascular disease that bordered on statistical significance (p ˆ 0:052). In overweight people with Type 2 diabetes, the UKPDS showed similar results, with the important additional finding that treatment with metformin (which did not increase hyperinsulinaemia or weight gain) produced better results than sulphonylureas or insulin (3). Reduction in mean HbA1c level from 8.0% in the conventional group to 7.4% in the metformin group was associated with a reduction in the risk of diabetes-related death by 42%, death from any cause by 32% and any diabetes-related endpoint by 32%. Metformin was not tested in lean people. The maintenance of blood glucose as close to the normal range as possible required a stepwise addition of hypoglycaemic agents when the glycaemic goals were not met (2). Subjects assigned to any of the sulphonylureas could be given metformin, and these oral agents could later be replaced by insulin. In addition, the dietary policy allowed subjects to be given hypoglycaemic agents when the glycaemic goal (FPG 20 units

insulin dependency, estimates of LADA prevalence required a population-based epidemiological perspective. The opportunity for this came through the UKPDS (19). The evidence has mounted from a host of studies that anti-GAD has a clear role in diagnosing slow-onset Type 1 diabetes in adults (20, 21, 23), and in predicting future insulin dependency (21). We tested this in the UKPDS by measuring islet call antibodies (ICA) and antibodies to glutamic acid decarboxylase (anti-GAD) at diagnosis in a sample of the UKPDS cohort (19). We found 6% were positive for ICA, and 10% for anti-GAD, with 12% having either ICA or antiGAD, and 4% having both (Table 25.1). These subjects appear to be cases of Type 1 diabetes or LADA as judged by both phenotypic (19) and genotypic (25) features. The presence of autoantibodies correlated particularly with phenotypic features consistent with Type 1 diabetes such as early age at diagnosis, lower body mass index (BMI) and reduced beta cell function (19). In the UKPDS, at all ages, the presence of autoantibodies conferred an increased likelihood of decompensation to insulin therapy (19). Thus 94% of subjects aged under 35 years positive for ICA, and 84% of those positive for anti-GAD at baseline, required insulin therapy after 6 years compared with 14% of seronegative cases. Clearly these findings should be taken into account when interpreting the findings of the UKPDS as an unknown, but significant, proportion of subjects who progressed to insulin therapy had Type 1 rather than Type 2 diabetes, which they initially appeared to have on clinical grounds. THE UKPDS AND HYPERTENSION The effect of treating hypertension was also studied in the same subjects (4), and confirmed

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that a `tight blood-pressure control policy' has significant benefits. Lowering mean blood pressure from 155=87 to 144=82 reduced the risk of microvascular complications by 37%, strokes by 44%, cardiac failure by 56% and diabetes-related deaths by 32% (4). This benefit is greater than that achieved by the reduction in mean HbA1c from 7.9 to 7.0% This hypertension `sub-study' showed no difference between the two anti-hypertensive agents used, captopril and atenolol (5). This is not surprising as most subjects required therapy with both agents, and 29% required three or more antihypertensive agents. Other studies have shown that ACE inhibitors are the first choice for hypertension therapy in diabetes, and that a blood pressure target of less than 130=80 is appropriate in most cases (26, 27). It appears from the UKPDS that as with diabetes therapy, `polypharmacy' is required to achieve this target and the improved outcome that results. The UKPDS has also emphasized that diabetic complications are common, are frequently present by the time of diagnosis, and significantly affect quality of life (2). The study reported that at diagnosis, 35% of subjects had retinopathy, and 15% had neuropathy. In this study, only 2% had proteinuria initially, but this may be because renal impairment was an exclusion criterion. After 12 years in the study, 11% had visual acuity below the level permissible for driving, 1% were legally blind, and 50% of men had erectile dysfunction. This was despite participation in a clinical trial with levels of control better than generally achieved in the community. SUMMARY The UKPDS has confirmed the DCCT (12), Stockholm (16) and Kumamoto (17) studies' findings that intensive therapy to improve glycaemic control reduces the risk of microvascular complications. It thus appears that in both Type 1 and Type 2 diabetes, each 1% reduction in HbA1c reduces the risk of microvascular complications by approximately 25%. The reduction in risk for macrovascular disease bordered on statistical significance. Control of blood pressure is even more important than blood glucose control (4). Although not studied in the UKPDS, other studies

have demonstrated the importance of control of dyslipidaemia (28, 29). This is certainly good news for people with Type 2 diabetes in terms of reducing the risk of coronary heart disease and stroke. But it also places a clear obligation on health providers to inform their patients accordingly, and offer treatment that meets targets of blood pressure, lipids and HbA1c levels that are lower than are currently being achieved without intensive therapy.

REFERENCES 1. UK Prospective Diabetes Study (UKPDS). VIII Study design, progress and performance. Diabetologia (1991); 34: 877± 890. 2. UK Prospective Diabetes Study Group. Intensive blood glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with Type 2diabetes (UKPDS 33). Lancet (1998); 352: 837± 853. 3. UK Prospective Diabetes Study (UKPDS) Group. Effect of intensive blood-glucose control with metformin on complications in overweight patients with Type 2 diabetes (UKPDS 34). Lancet (1998); 352: 854± 865. 4. UK Prospective Diabetes Study Group. Tight blood pressure control and risk of macrovascular and microvascular complications in Type 2diabetes: UKPDS 38. Br Med J (1998); 317: 703± 713. 5. UK Prospective Diabetes Study Group. Efficacy of atenolol and captopril in reducing risk of macrovascular and microvascular complications in Type 2 diabetes: UKPDS 39. Br Med J (1998); 317: 713±720. 6. UK Prospective Diabetes Study Group. Cost effectiveness analysis of improved blood pressure control in hypertensive patients with Type 2 diabetes: UKPDS 40. Br Med J (1998); 317: 720± 726. 7. The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care (1997); 20: 1183±1197. 8. Alberti KGMM, Zimmet PZ for the World Health Organization Consultation. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and classification of diabetes mellitus. Provisional Report of a WHO Consultation. Diabetic Med (1998); 15: 539±553. 9. Charles MA, Balkau B. Revision of diagnostic criteria for diabetes (Letter). Lancet (1996); 348: 1657±1658.

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UKPDS: AN EPIDEMIOLOGICAL PERSPECTIVE

10. University Group Diabetes Program. A study of the effects of hypoglycemic agents on vascular complications on patients with adult-onset diabetes: Evaluation of phenformin therapy. Diabetes (1975); 24 (suppl 1): 65 ± 184. 11. Zimmet P: Hyperinsulinaemia Ð how innocent a bystander. Diabetes Care (1993); 16: 56 ± 70. 12. Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med (1993); 329: 977± 986. 13. The World Health Organization Multinational Study of Vascular Disease in Diabetics. Prevalence of small vessel and large vessel disease in diabetic patients from 14 centres. Diabetologia (1985); 28 (Suppl): 615±640. 14. Hamman RF. Epidemiology of microvascular complications. In: KGMM Alberti, P Zimmet, RA DeFronzo (eds), International Textbook of Diabetes Mellitus. 2nd edn. Chichester, Wiley, 1997: pp. 1293± 1319. 15. Zimmet P, Dowse G, Finch C, Serjeantson S, King H. The epidemiology and natural history of NIDDM Ðlessons from the South Pacific. Diabetes= Metabol Rev (1990); 6: 91±124. 16. Reichard P, Nilsson BY, Rosenqvist U. The effect of long-term intensified insulin treatment on the development of microvascular complications of diabetes mellitus. N Engl J Med (1993); 329: 304± 309. 17. Ohkubo Y, Kishikawa H, Araki E et al. Intensive insulin therapy prevents the progression of diabetic microvascular complications in Japanese patients with non-insulin-dependent diabetes mellitus: a randomized prospective 6-year study. Diabetes Res Clin Pract (1995); 28: 103±117. 18. WHO Expert Committee on Diabetes Mellitus. Second Report. Geneva: WHO, 1980. 19. Turner R, Stratton I, Manley S et al. UKPDS 2: Autoantibodies to islet-cell cytoplasm and glutamic acid decarboxylase for prediction of insulin requirement in Type 2 diabetes. Lancet (1997); 350: 1288± 1293. 20. Tuomi T, Groop LC, Zimmet PZ, Rowley MJ, Knowles W, MacKay IR. Antibodies to glutamic acid decarboxylase reveal latent autoimmune

21. 22.

23.

24.

25.

26.

27.

28.

29.

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diabetes mellitus in adults with a non-insulindependent onset of disease. Diabetes (1993); 42: 359± 362. Zimmet P, Turner R, McCarty D, Rowley M, Mackay IR. Crucial points at diagnosisÐ NIDDM or slow IDDM. Diabetes Care (1999); 22: B59 ± B64. Neufeld ND, Raffel LJ, Landon C, Chen Y-DI, Vadheim CM. Early presentation of Type 2 diabetes in Mexican-American youth. Diabetes Care (1998); 21: 80 ± 86. Hagopian WA, Karlsen AE, Gottsater A et al. Quantitative assay using recombinant human islet glutamic acid decarboxylase (GAD65) shows that 65K autoantibody positivity at onset predicts diabetes type. J Clin Invest (1993); 91: 368± 374. Zimmet PZ, Tuomi T, Mackay R, Rowley MJ, Knowles W, Cohen M et al. Latent autoimmune diabetes mellitus in adults (LADA): the role of antibodies to glutamic acid decarboxylase in diagnosis and prediction of insulin dependency. Diabetic Med (1994); 11: 299± 303. Horton V, Stratton I, Zimmet P et al. Autoimmune diabetes assessed by ICA, anti-GAD and DR3=4 in population based cohort of patients with NIDDM. Diabetes (1995); (suppl 1): A236. Estacio OE, Jeffers BW, Hiatt WR et al. The effect of nisoldipine as compared to enalapril on cardiovascular outcomes in patients with non-insulin dependent diabetes and hypertension. N Eng J Med (1998); 338: 645± 52. Tatti P, Pahor M, Byington RP et al. Outcome results of the fosinopril versus amlodipine cardiovascular events randomised trial (FACET) in patients with hypertension and NIDDM. Diabetes Care (1998); 21: 597± 603. PyoraÈlaÈ K, Pedersen TR, Kjeksus J et al. Cholesterol lowering with simvastatin improves prognosis of diabetic patients with coronary heart disease: a subgroup analysis of the Scandinavian Simvastatin Survival Study (4S). Diabetes Care (1997); 20: 614± 620. Sacks FM, Pfeffer MA, Moye LA et al. The effect of pravastatin on coronary events after myocardial infarction in patients with average cholesterol levels: Cholesterol and Recurrent Events Trial Investigators. N Engl J Med (1996); 335: 1001± 1009.

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Index Note: Page references in italics refer to figures; those in bold refer to tables 1PF-1 20 Aboriginal communities 44 ± 6, 47 see also Native Americans acanthosis nigricans 21, 22, 115, 123± 4, 146 acarbose 44 accelerometers 53 ACE inhibitors 329, 343, 377 acromegaly 21 activity monitors 53 Addison's disease 19 adenocarcinoma 21 adenovirus 21 adiposity 289± 90 Africa 225± 31 body mass index and waist ± hip ratio 230 epidemicity index 226± 8 ethnic differences 228± 9 family history 230± 1 gender distribution 229± 30 impact of age 230 impaired glucose tolerance 226± 8 longitudinal studies 231 physical activity 230 prevalence 225±6, 229, 231 total glucose intolerance (TGI) 226±8 urban=rural differences 228 African Americans 157± 72 clinical variants 165± 8 atypical diabetes of childhood 167 diabetic ketoacidosis 166± 7 insulin-dependent diabetes mellitus in children 167± 8 remission in diabetes 165± 6 complications of diabetes 168± 72 amputations and peripheral vascular disease 170 cardiovascular disease 170±2 end-stage renal disease (ESRD) 168± 70 mortality of diabetes 172 nephropathy 168± 70 retinopathy 168 hyperinsulinemia 162 incidence 159 metabolic insulin resistance syndrome 162 pathogenesis of Type 2 diabetes 162± 5 insulin-sensitive and -resistant variants 162 ±5 pancreatic -cell failure 165 prevalence 158

risk factors for non-insulin-dependent diabetes 159± 62 age, sex and family history 159 candidate genes, diet and physical activity 159± 60 impaired glucose tolerance (IGT) 160 insulin resistance 161± 2 obesity 160 regional obesity 160± 1 socio-economic status 160 age and duration of diabetes 306 albuminuria 359± 60 alcohol 136, 358 aldosteronoma 21 amputations 170, 189±90, 321 prevention of 333± 4 ankle=brachial index (ABI) 322 anti-insulin receptor antibodies 22 Argentina Type 1 diabetes in 205± 6 Type 2 diabetes in 209± 10 ascertainment 65 ±7, 66 of Type 1 diabetes 72 ± 4 ATP binding cassette (ABC) superfamily 145 atypical Type 1 diabetes in young people 113± 27, 167± 8 etiologic hypothesis 114± 15 range of diabetes in youth 114 azathioprine 93 -cell function genetic defects of 20 ± 1 in Type 1 and Type 2 diabetes 120± 1 biguanides 294 biochemical definition 9 blood pressure 311, 356± 7 body mass index (BMI) 20, 230, 360 borderline diabetics 9 Brazil Type 1 diabetes in 206± 7 Type 2 diabetes in 210± 11 breastfeeding 85 ± 6, 104 Caltrac 53 candidate genes 144± 6 Captopril 343, 377 capture-recapture method carbohydrate 134 ±5

66, 74

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432

INDEX

cardiovascular disease (CVD) 42, 170± 2, 188± 9, 301, 339 carnitine metabolism 329 case ascertainment 66 cassava consumption 266± 7 chemical-induced diabetes 21 chemical toxins 34 Chicago Childhood Diabetes Registry 124± 6 data-based classification of early Type 2 diabetes 124 distinguishing Type 2 from Type 1 diabetes 125± 6 early Type 2 diabetes incidence 124± 5 time trends in incidence 124 Chile Type 1 diabetes in 207 Type 2 diabetes in 211 children, onset in 124± 6 see also atypical Type 1 diabetes China 247± 51 changing prevalence 250 future 251 impaired glucose tolerance and effects of intervention 250 National Prevalence Survey (1994) 248±50 Type 1 diabetes 247 Type 2 diabetes 247± 8 in DaQing 248 cholesterol, blood 357 chromium 136 classification of diabetes mellitus syndrome clinical 17 etiological 15 new 14 ± 16, 18 ± 19 new etiological types 18 ± 21 previous 14 claudication 321 clinical diabetic nephropathy 337 clinical syndrome 8 clinically significant macular oedema (CSME) 349 Colombia Type 1 diabetes in 207 Type 2 diabetes in 211 COMMA 66 ± 7 coronary heart disease 301± 12 incidence studies 303± 5 natural history 305± 6 prevalence studies 301± 3 risk factors for development 306± 12 age and duration of diabetes 306 blood pressure 311 glycemia 307± 8 insulin 308± 10 exogenous 310 lipids and lipoproteins 310± 11 metabolic syndrome 311 microalbuminuria 311± 12 sex 306±7 costs, economic 383± 94 direct and indirect estimates 385± 6

direct medical 384 estimates faced by patients 387 indirect morbidity and mortality 384± 5 opportunity 386 outcomes research 392± 3 supply of and demand for care 393 using resources unwisely 388± 92 economic evaluation 388± 9 estimates from evaluation studies 389± 92, 390± 1 cow's milk protein 34, 85 ± 6, 104 Coxsackie B virus 21, 33, 84, 85, 104 Coxsackie B3 virus 84 Coxsackie B4 virus 84, 85 C-peptide 106, 120± 1 Cuba Type 1 diabetes in 207 Type 2 diabetes in 211± 12 Cushing's syndrome 21 cyclosporin A 93 cystic fibrosis 21 cytomegalovirus 21, 33, 104 DAISY (Diabetes Autoimmunity Study in the Young) 37 DaQing 248 DIABALT group 72 Diabetes Control and Complications Trial (DCCT) 2, 120 Diabetes Epidemiology Research International Group (DERI) 71 Diabetes Prevention Program (DPP) 43 diabetic foot 331 callus 332 epidemiology of problems 332± 3 foot pressure abnormalities 332 peripheral neuropathy 332 peripheral vascular disease 331± 2 risk factors 331± 2 diabetic ketoacidosis 166± 7 diabetic nephropathy 337± 44 definitions and natural history 337 etiology and risk factors 339± 41 future perspectives 343 genetic and other non-modifiable risk factors 341 HLA system 341 incidence 338 microalbuminuria 339 mortality 339 prevalence 337± 8 prospects for international collaborative research 343± 4 see also diabetic retinopathy diabetic neuropathy 327± 34 assessment for epidemiological studies 331 epidemiology 329±30 etiology 327 ±9 prevention of ulceration and amputation 333± 4 see also diabetic foot

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433

INDEX

diabetic retinopathy 168, 189, 341± 3, 349± 63 classification 349± 51 incidence 351±4, 355 in Mexico 199± 200 prevalence 351, 352± 4 prevention of blindness 362± 3 relationship to visual outcome and mortality 361 risk factors 354± 61 treatment 361± 2 diabetic state, definition 7 ±8 diagnosis and diagnostic criteria 11 ± 12, 13 DIDMOAD 144 diet 34, 133± 6, 186 DIPP (Diabetes Prediction and Prevention Project) 37 double diabetes 115, 118± 19 doubly-labeled water (DLW) 53 Down's syndrome 22 drug-induced diabetes 21 duration of diabetes 306, 354 dynamic prediction models 68, 69 dyslipidemia 310, 374± 6 early Type 2 diabetes 124± 6 proposed diagnostic criteria 126 temporal trends, ethnicity and distinctive features 115± 17 endocrinopathies 21 end-stage renal disease (ESRD) 168± 70 end-stage renal failure (ESRF) 337 enterovirus 84 ± 5 environmental factors modifying 91 ± 4, 137 Type 1 diabetes and 85 ±6 Type 2 diabetes and 133±8 epidemicity index 226± 8 Ethiopia, malnutrition in 268± 9 etiology of diabetic nephropathy 339± 41 of diabetic neuropathy 327±9 of Type 1 diabetes genetic contribution 103 non-genetic contribution 103± 5 of Type 2 133± 8 euglycemic insulin clamp 161 EURODIAB 71 fat, dietary 135± 6 fatty acid metabolism 329 fibre, dietary 134± 5 fasting insulin resistance index (FIRI) 286± 7 fasting plasma glucose 13, 14 Fatty Acid Binding Protein 2 (FABP2) gene 146 fibrocalculous pancreatic diabetes (FCPD) 1, 17, 264± 9 cassava consumption 266± 7 clinical features 265 diabetes component 265± 6 familial and genetic factors 267

geography 264±5 malnutrition 266 possible causative factors 266 prevalence and incidence 265 unresolved questions 267± 8 fibrocalculous pancreatopathy 21 field surveys 399± 421 after the survey 418± 21 data processing and reporting 420± 1 packing, transportation and storage of equipment=specimens 418± 19 public relations 418 safe-keeping of survey forms=documentation 419± 20 need for 399± 400 organisation and conduct 411± 18 census of survey population 415± 16 dates, staff, survey sites, equipment and supplies 411± 14 pre-testing, pilot-testing and staff training 414± 15 promotion and maximizing response 416± 17 supervision and quality control 417± 18 planning and preparation 400± 11 choice of research method 401 choice of study variables and measurement instruments 402± 4 core documents 405± 6 defining study objectives 400 design of questionnaires 404 documentation 405± 9 equipment and supplies documentation 407± 8 ethics approval 410± 11 funding applications 410 planning analyses and data presentation 410 sample size determination 402 selection of study population and sampling method 401±2 subject information forms 406± 7 survey process and laboratory documentation 408± 9 Flatbush diabetes 119, 121 foot-and-mouth disease 84 foot ulcers 321 forecasting 68 frequently sampled intravenous glucose tolerance test (FSIVGTT) 161, 286 GAD antibodies 360 gemfibrozil 311 gender 229± 30, 276, 356 as risk factor for diabetic retinopathy genetics 360 of Type 1 diabetes 86 ± 91 familial clustering 86 ± 7 HLA system 87 ±90 non-HLA genetic markers 90 ± 1 of Type 2 diabetes 141± 8 screening 91 ± 4

306± 7

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434

INDEX

Gestational Diabetes Mellitus (GDM) 1, 14, 17, 22 ± 3, 199 gestational impaired glucose tolerance (GIGT) 17 glucagonoma 21 glucokinase 20, 121± 2 glucose blood 9, 13, 25 in urine 25 ±6 tolerance, impaired 288 glutamic acid decarboxylase (GAD) 85, 106 glutamic acid decarboxylase antibodies (GADA) 33, 36, 120 glycated hemoglobin=blood glucose control 356 glycemia 307± 8 glycogen synthase gene (GSY1) 146 Graves' disease 19 haptocyte nuclear factor (HNF) HNF-1 20, 142± 3, 145 HNF-1 142± 3 HNF-4 20, 142, 143, 145 HNF-12 20 Harvard Alumni Questionnaire 54, 56 Hashimoto's thyroiditis 19 health economics 383± 4 Health of the Nation, The 68 heart rate monitoring 53 ± 4 hemochromatosis 21 history 7, 133 HLA system 87 ± 90, 103, 121± 2, 341 HOMA (Homeostasis Model Analysis) 286 hormonal influences 359 hyperglycemia 9, 16, 21, 126, 311, 328 definition 7 ± 8 hyperinsulinemia 21, 162, 287± 8 epidemiology of 122 insulin resistance and 122± 3 hypertension 171± 2, 289, 340 familial predisposition 341 hypertriglyceridemia 288± 9, 311 hyperuricemia 290 hypo-HDL-cholesterolemia 288± 9 IDDM1 31 IDDM2 31 ± 2 idiopathic Type 1 diabetes 19 IDX-1 143 IKATP 145 immune-mediated diabetes mellitus 19 impaired fasting glycemia (IFG) 16, 18, 24 impaired glucose tolerance (IGT) 1, 14, 16, 160 new classification 18 incidence 65, 67 ± 8 age specific 78, 79 ± 80 seasonal variation 83 ±4 sex ratio 78, 81 sex ratio by age group 78 ±83

temporal trends 77 ± 8 Type 1 diabetes 74, 75 ± 6 incipient nephropathy 170 insulin 308± 10 insulin action, genetic defects 21 insulin autoantibodies (IAA) 33, 36, 106 insulin receptor kinase (IRS-1) 146 insulin resistance syndrome 18, 122± 3, 161± 2, 285± 95 consequences and treatment 293±4 definition 285 ±6 evidence for 290± 2 measuring elements in 286± 90 prevalence, incidence and risk factors 292± 3 intervention 137 IPF1 142, 143, 145 islet autoantibodies 119± 20 Islet Brain 1 (IB1) 145 islet cytoplasmic antibodies (ICA) 33, 36, 106 Jamaica, Type 2 diabetes in 212 Japan 253± 9 incidence and prevalence study 253± 7 Type 1 diabetes 253± 5 clinical characteristics at diagnosis 254 incidence=prevalence in adults 254± 5 overall and age-specific incidence 253 prevalence 253± 4 Type 2 diabetes 255± 7 characteristics at onset in children 256 epidemiology in children 256 prevalence 255± 6 risk factors for developing 256± 7 mortality study 257± 9 Type 1 diabetes 257± 8 causes of death 257 long-term mortality 257 risk factors for premature death 257±8 Type 2 diabetes 258± 9 changing pattern of causes of death 258±9 mortality rate 258 J-type (Jamaican type) diabetes 263 see also Malnutrition Related Diabetes Mellitus juvenile tropical pancreatitis syndrome see Malnutrition Related Diabetes Mellitus ketoacidosis 17, 20 ketone bodies in blood 26 in urine 26 ketosis resistant diabetes see Malnutrition Related Diabetes Mellitus Kleinefelter's syndrome 22 kwashiokor 270 Large Scale Integrated (LSI) activity monitor 53 latent autoimmune diabetes in adults (LADA) 19, 41, 114

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INDEX

Latin America 205± 13 Type 1 diabetes in 205± 8 Type 2 diabetes in 208± 13 leprechaunism 21, 146 leptin 280 life expectancy 69 lifestyle, Type 2 and 8 lifetime physical activity measurement 55 lipids 310± 11 lipoproteins 310± 11 lower extremity arterial disease (LEAD) 320± 2, 323 amputations 321 ankle=brachial index (ABI) 322 claudication 321 foot ulcers 321 pulse deficit 322 risk factors 322 macrovascular disease 341± 3 magnesium 136 Malnutrition Related Diabetes Mellitus (MRDM) 1, 14, 17, 263± 70 historical background 263± 4 see also fibrocalculous pancreatic diabetes; protein-deficient pancreatic diabetes; protein-deficient diabetes mellitus marasmus 270 maternally inherited diabetes and deafness (MIDD) 141, 144 Maturity-onset Diabetes of Youth (MODY) 20, 41, 47, 114, 117, 141±4, 157 glucokinase mutations 142 mutations in transcription factor genes 142± 3 MELAS syndrome 20, 144 Mendenhall's syndrome 328 meningovirus 84 metabolic syndrome (Syndrome X) 285, 291, 311 see also insulin resistance syndrome metformin 43, 294 Mexico 195± 203 diabetes and pregnancy 199 diabetes in early adulthood 198±9 diabetes-related complications 199± 201 epidemiology Type 1 diabetes in 207 Type 2 diabetes in 212 insulin-dependent diabetes mellitus 199 natural history of glucose intolerance 199 prevalence of diabetes 196± 8 microalbuminuria 311± 12, 339 Middle East 217± 21 long-term complications 220± 1 Type 1 diabetes 220 Type 2 diabetes 217± 20 Minnesota Leisure-time Physical Activity Questionnaire (MLTPQ) 54 miscellaneous medications 358 mitochondrial diabetes 143± 4

435

Modifiable Activity Questionnaire 56 Modifiable Physical Activity Questionnaire 54 Modified Baecke questionnaire 56 mononeuropathies 327±8 mortality 172, 369± 77 cost 384± 5 diabetic nephropathy and 339 diabetic retinopathy and 361 dyslipidemia 374± 6 glycemic control 377 hypertension (and endothelial function) 376 international comparisons 371±3 in Japan 257± 9 micro-albuminuria as a risk factor 376± 7 modifiable cardiovascular risk factors 374 risk factors 371 time-related variables and albuminuria 370± 1 Type 1 370 Type 2 373± 4 Multinational Project for Childhood Diabetes (DIAMOND) (WHO) 71 ± 2, 72, 205± 13, 247 mumps 21, 84 Native Americans, diabetes in 181± 90 complications of Type 2 diabetes 187±90 determinants of Type 2 diabetes 184± 7 environmental factors 185± 7 genetic factors 184± 5 incidence 184 non-vascular complications 190 perinatal factors 185 periodontal disease 190 prevalence 181±4 vascular complications 187± 90 cardiovascular disease 188± 9 lower-extremity amputations 189± 90 renal disease 189 retinopathy 189 stroke 189 neonatal nutrition 34 nephropathy 168± 70, 342 nerve growth factors (NGF) 329 nerve ischemia 329 NeuroD1 (Beta2) 142, 143, 145 nicotinamide 105 nitrates 34 nitrites 34 nitrosamines 34, 86, 104 NOBADIA (Norwegian Babies against Diabetes) non-enzymatic glycosylation 329 normoglycemia 16 new classification 18 normotension 170 obesity 160± 1, 185± 6, 289± 90 definition 122 duration and timing 273± 5 epidemiology of 122

37

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436 obesity (continued ) fat distribution 275± 7 importance of fatness cf. 275± 6 gender, diabetes and 276 overall fat mass, diabetes and 276± 8 genetic susceptibility and 277± 9 ethnic group 279 family history 277± 9 mechanisms linking 279± 80 physical activity and 279 and Type 2 diabetes 273±80 oral glucose tolerance test (OGTT) 11, 12 ± 13, 12, 24 ± 5, 66 overt nephropathy 170 Pacific Island populations 239± 43 environmental risk factors for Type 2 240± 2 decreasing physical activity 242 diet 242 epidemic obesity 242 thrift genotype, thrifty phenotype 242± 3 Type 2 diabetes prevalence 239± 40 impaired glucose tolerance (IGT) 240 undiagnosed diabetes 240 pancreas, exocrine, disease of 21 pancreatectomy 21 pancreatic carcinoma 21 pancreatic diabetes see Malnutrition Related Diabetes Mellitus pancreatic islet -cell destruction 27 pancreatitis 21 Paraguay Type 1 diabetes in 207 Type 2 diabetes in 212± 13 PDX-1 143 pectin 134 pedometers 53 pentamidine 21 peripheral vascular disease 170 Peru Type 1 diabetes in 207± 8 Type 2 diabetes in 213 phenformin 43 pheochromocytoma 21 physical activity 186± 7, 358± 9 comprehensive survey 56 ± 7 definition 51 ± 2 health-related dimensions 52 measurement 51 ± 8 measurement tools 52 ± 7 objective approaches 52, 53 ± 4 subjective approaches 52, 54 ± 6 population and outcome considerations 57 ± 8 Type 2 diabetes mellitus 58 types measured 55 ± 6 Physical Activity Recall Questionnaire 54 physical fitness measurement 54 physical inactivity 137

INDEX

Physical Scale for the Elderly (PASE) 56 Pioglitazone 295 plasma insulin levels 161 plasminogen activator inhibitor 1 (PAI-1) 290 plurimetabolic syndrome see Syndrome X poliomyelitis 84 polycystic ovary syndrome 106, 123± 4 polyneuropathies 328± 9 polyol pathway activity 328± 9 post-prandial hyperglycemia 22 PPAR gamma 145 prediction of Type 1 diabetes 105± 6 available markers 106± 7 hypothetical example 108± 10 methodological considerations 107± 8 prednisolone 93 pregnancy 359 see also gestational diabetes mellitus prevalence 1 ± 2, 65, 67 prevalence pool concept 68 prevention of Type 1 diabetes 31± 7, 91 ± 4 determinants chemical toxins 34 Coxsackie B virus infection 33 environmental 33± 4 genetic 31 ± 4 neonatal nutrition 34 viral infection 33 ± 4 disease process 35 prevention strategies 36 ±7 stages in development 35 ± 6 protein 136 protein-deficient diabetes mellitus (PDDM) see protein-deficient pancreatic diabetes protein-deficient pancreatic diabetes or (PDPD) 268 malnutrition and prevalence 269 proteinuria 359± 60 Puerto Rico, Type 1 diabetes in 208 pulse deficit 322 Rabson ±Mendenhall syndrome 21 remission in diabetes 165±6 renal disease 189 reovirus type 3 84 retinal-renal syndrome 342 retinopathy see diabetic retinopathy retrovirus 34 risk factors 122 Rosiglitazone 295 rubella 21, 33, 84 simvastatin 375 single nucleotide polymorphisms (SNPs) 145 smoking 136, 357± 8 socio-economic factors 358 somatostinoma 21 sorbitol 328

2, 17,

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437

INDEX

South East Asia 233± 7 epidemiology of Type 1 diabetes 237 epidemiology of Type 2 diabetes 233± 4 familial aggregation in Type 2 236 obesity 236±7 prevalence of IGT 235± 6 risk factors for Type 2 236 urban ± rural differences 234± 5 static prediction models 68, 69 Steno Hypothesis 341 STF-1 143 stiff man syndrome 22 stretozotocin 34 stroke 189, 320, 322± 3 sucrose 134 Syndrome X (metabolic syndrome) 285, 291, 311 see also insulin resistance syndrome systemic lupus erythematosus 22 temporal trends 68 ±9 terminology, changes in 16 ± 17 thiazolidinedione 294 thrifty genotype hypothesis 202, 220, 242± 3, 279± 80 time-frame 54 ± 5 tolbutamide 43 total glucose intolerance (TGI) 226± 8 transplantation 169 Trial to Reduce IDDM in Genetically at Risk (TRIGR) 34 Trinidad, Type 2 diabetes in 213 Tritrac 53 Troglitazone 43, 44, 294, 295 tropical diabetes see Malnutrition Related Diabetes Mellitus tropical pancreatic diabetes see Malnutrition Related Diabetes Mellitus Turner's syndrome 22 Type 1 diabetes mellitus 19 ascertainment 72 ± 4 incidence worldwide 74, 75 ± 6 worldwide registration 71 ± 2 Type 2 diabetes mellitus 19± 20 early-onset, temporal trends, ethnicity and distinctive features 115± 17

etiology and environmental factors 133± 8 importance of classification in prevention 41 in indigenous populations 44 ± 7 socio-cultural perspectives of prevention 46 ± 7 physical exercise measurement 58 positional cloning of genes 146± 7 prevalence 41± 8 primary prevention 42 ± 6 behavioural interventions (exercise and=or diet) 42 ± 3 pharmacological interventions 43 ± 4 population-based prevention projects 44 ± 6 Type A insulin resistance 21 Type B insulin resistance 22 ulceration 321 prevention of 333± 4 United Kingdom Prospective Diabetes Study (UKPDS) 2, 425± 8 classification issues in 427 findings 426±7 hypertension and 427± 8 importance 425 importance of metabolic control 425± 6 urinary albumin excretion rate (UAER) 337 Vacor 21, 34, 86 Venezuela, Type 1 diabetes in 208 viral infections 33 ± 4, 84 ± 5, 104 vitamin D deficiency 136 vitamin E 136 waist ± hip ratio 230 Wolfram's syndrome 22, 143± 4 Yale Physical Activity Survey (YPAS)

56

zinc 136 Z-type (Zuidema syndrome) diabetes 263 see also Malnutrition Related Diabetes Mellitus Zuni Diabetes Program, New Mexico 46 Index compiled by Annette Musker